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Recent evidence notes

Use these dated reports to understand a specific capacity or platform claim, then return to the evergreen guides below for the buying decision.

New model-fit guide: Muse Glimmer 30B VRAM requirements compares Meta’s official 24GB and 32GB Q4 paths, separate vision and DFlash files, 131K context boundary and 64GB BF16 target.

New consumer deployment guide: Run Muse Glimmer 30B with llama.cpp, Ollama or LM Studio, then add vision or DFlash only after the text smoke test passes.

Buying for Muse Glimmer? Compare the official 24GB Q4_K_M and 32GB dynamic-Q4 hardware tiers without turning capacity into an unsupported speed ranking.

New deployment guide: How to run Muse Spark Open maps the official SGLang, vLLM, TensorRT-LLM and distributed-MLX paths while keeping llama.cpp and Unsloth quantized artifacts separate.

• DeepSeek V4.1 Flash in llama.cpp — why a recommended 2-bit download still occupies about 249GB.
• Flint’s 23GB coding-model weights — why a nominal 24GB device can be tight and 48GB changes the deployment margin.
• Ryzen AI Halo with 192GB — capacity evidence separated from speed and value claims.
• RTX Spark for local AI — what the compact system’s published hardware can and cannot establish.
• Olares One for home AI — a 24GB laptop-GPU system where convenience, software and price matter alongside capacity.
• Qwen 4: confirmed evidence versus speculation — planning hardware without treating an unconfirmed model release as a specification.

Choose hardware by model size

Start with the closest workload class, then validate the exact artifact, context, runtime and concurrency. These are capacity-first buying paths, not GPU speed rankings.

Model class	Buyer guide	Typical decision boundary
7B–8B	Best GPU for a 7B or 8B LLM	8GB, 12GB or 16GB
14B	Best GPU for a 14B LLM	12GB, 16GB or 24GB
gpt-oss-20b	Best GPU for gpt-oss-20b	OpenAI ~16GB MXFP4; 24GB/32GB headroom; ~48GB bfloat16
24B	Best GPU for a 24B LLM	16GB constrained; 24GB Q4/Q5; 32GB Q8; 64GB+ BF16 planning
27B	Best GPU for a 27B LLM	24GB Q4; 32GB headroom; 48GB practical Q8
30B–32B	Best GPU for a 30B–32B LLM	24GB, 32GB or 48GB
70B	Best GPU for a 70B LLM	48GB constrained paths; 64GB, 80GB or 96GB
gpt-oss-120b	Best hardware for gpt-oss-120b	OpenAI ≥60GB MXFP4 floor; H100 80GB, 96GB or deliberate multi-GPU

Already know the model and settings? Use the LLM VRAM calculator for an explainable planning range, then validate in the intended runtime.

Get a local model running

Your first local AI chat
Follow the graphical LM Studio walkthrough for supported Windows PCs and Apple silicon Macs. Download a modest model, get an answer and check whether the same chat works offline.

Validation: Steps checked against official documentation; not independently tested end to end. The offline check is not a complete privacy audit.

Stuck on a download, model load or slow answer? Go to the guide’s first troubleshooting checks.

Understand memory and buying risks

VRAM vs unified memory: what matters for local AI
Separate dedicated GPU memory, system RAM and Apple unified memory. Account for model weights, context, concurrent requests and runtime overhead before treating a capacity label as a fit guarantee.

Buying a used GPU for local AI: a practical checklist
Check the exact card, system fit, seller evidence, return terms and whole cost. Use the arrival checks to investigate problems promptly; the checklist cannot certify a card’s condition or remaining life.

Can you compare GPUs by AI TOPS?
Understand capacity, bandwidth, precision, accumulation and dense versus sparse peak metrics before comparing GPUs. This sourced guide explains published specifications; it does not report our own GPU benchmarks.

How much VRAM do you need for a 7B LLM?
Compare 4GB through 16GB tiers using official Qwen2.5-7B GGUF sizes, explicit KV-cache arithmetic and complete-workload headroom.

Choosing between 8GB, 12GB and 16GB?
Compare mainstream memory tiers using official 7B and 14B artifact sizes, context headroom and exact GPU-variant checks.

RTX 3060 12GB vs 4060 Ti 16GB vs 5060 Ti 16GB
Compare mainstream NVIDIA paths by exact capacity, architecture, CUDA generation, power and verified software fit—not gaming charts or peak metrics.

RTX 5060 Ti 16GB for local AI
Check 7B, 14B and constrained 32B model fit, Blackwell/CUDA capability 12.0 software support, 16GB-versus-8GB identity and complete-system requirements.

RTX 4060 Ti 16GB for local AI
Evaluate 7B, 14B and constrained 32B model fit, Ada/CUDA capability 8.9 support, exact 16GB-versus-8GB identity and partner-card requirements.

RTX 3060 12GB for local AI
Check 7B, constrained 14B and offload-only 32B paths, exact 12GB-versus-8GB identity, Ampere support and used-card evidence.

Plan memory capacity

AMD Instinct MI210 64GB for local AI
Understand why a used 64GB HBM accelerator needs server airflow, EPS12V power, a separate management/display path and a verified ROCm stack.

Radeon PRO W7900 48GB for local AI
Check ROCm, operating-system and application compatibility before comparing AMD’s 48GB ECC workstation card with CUDA alternatives.

RTX PRO 6000 Blackwell 96GB for local AI
Evaluate the one-GPU 96GB CUDA tier, including 70B model fit, 1,792GB/s published bandwidth, 600W board power and Workstation-versus-Server edition checks.

DGX Spark and ASUS Ascent GX10 for local AI
Understand 128GB coherent unified memory, Arm/DGX OS compatibility, published bandwidth, multi-system scaling and comparisons with discrete GPUs and Mac Studio.

24GB vs 48GB VRAM for local AI
Decide whether one larger dedicated-memory pool is worth the move from consumer to professional GPUs, with explicit 70B, multi-GPU, ECC and offload caveats.

How much VRAM do you need for a 70B LLM?
Compare 24GB through 96GB memory tiers using a published Qwen2.5-72B artifact, explicit KV-cache arithmetic, multi-GPU caveats and CPU-offload tradeoffs.

Mac Studio vs RTX 5090 for local AI
Separate large shared unified-memory capacity from 32GB dedicated VRAM, then check CUDA versus MLX/Metal support, bandwidth and whole-system requirements.

RTX 3090 vs 4090 vs 5090 for local AI
Compare the 24GB and 32GB consumer-GPU tiers without treating generation, capacity and measured application speed as the same question.

Test the workload you care about

Browse the local AI benchmark database
Inspect cited measurements from external testers, with historical source dates, reported settings, explicit unknowns and downloadable data. Results stay grouped by source and test phase.

Test your local AI workload before you buy
Choose pass/fail criteria, keep the model and settings fixed, and record answer quality, waiting time and memory observations. Includes read-only checks for Ollama and NVIDIA GPUs, plus Activity Monitor observations on a Mac.

What you get: A testing procedure and fields to copy into your own record. This guide does not supply measured hardware rankings or an automated benchmark runner.

Practice with checkable tasks

Extract fictional invoices into checked CSV with local AI
Turn seven fictional plain-text invoices into CSV, preserve missing fields and flag inconsistent totals. Compare the untouched response with an answer key rather than judging it by appearance.

Download the invoice practice package (ZIP): source documents, ready-to-copy prompts, expected CSV, an explained answer key, a run-record template and a Python validator. Read the exercise before running the checker.

Requirements: An already working local text-chat model and Python 3 for the checker. The checker uses the Python standard library and does not call a model.

Validation: The validator has been software-tested; the local-model walkthrough has not yet been run. This small exercise does not test OCR, establish model rankings or measure hardware speed.

Turn a scanned PDF into searchable text locally
Try a Linux cloud-tested OCR exercise with fictional scans, searchable output and a checker. Review the documented identifier error and a separate corrected transcript; the raw searchable PDF still contains the known text-layer error. macOS and Windows are unverified.

Found an unclear step or a broken link? Use the Community correction checklist to report the page, what happened and your software version. Keep private files and account details out of public reports.

For new reporting, add our RSS feed to your reader. Browse Guides & Tools for the full practice sequence. The feed does not announce every guide revision.

Choose a next step: ask a question with your goal, current hardware and blocker; share a benchmark result; or follow the weekly update by RSS.

Want a concise weekly selection of useful experiments, hardware developments and community discussions? See HighVRAM Weekly Digest and its signup status. The latest web edition covers screened offers, exact VRAM planning and Muse Spark’s official 700GB versus alternate 512GB Mac paths. Email signup is not open yet; RSS remains available.

CUDA vs ROCm for local AI
Verify the complete GPU, operating-system, driver, framework, runtime, kernel and frontend chain before treating either software stack as compatible.

Can you combine VRAM from two GPUs?
Understand why aggregate capacity is not one automatic pool, how runtimes split layers or tensors, and what topology, overhead and per-device headroom change.

RTX 5090 vs RTX A6000 for local AI
Compare newer 32GB Blackwell with an older 48GB Ampere professional card without confusing memory capacity, published bandwidth and measured application speed.

Take a break with Just One More GPU

Read the latest local-AI and home-lab comic, browse the three-issue archive, or suggest a situation for a future edition. The comic is a recurring community feature, not hardware-buying evidence.

Q4_K_M vs Q5_K_M vs Q6_K vs Q8_0
Choose a GGUF quantization using complete memory fit, runtime support and measured task quality—not the number in the filename alone.

CPU offload for local LLMs
Understand how hybrid CPU/GPU placement can reduce required VRAM, what still consumes system RAM, and how to test whether the result is actually usable.

How much Mac Studio unified memory for local AI?
Choose among current 36GB-through-512GB M5 Max and M5 Ultra configurations using exact artifacts, context, OS headroom and software support.

NVIDIA Tesla P40 24GB for local AI
Evaluate the low-cost 24GB server accelerator through model fit, passive cooling, CPU 8-pin power and the CUDA 13 Pascal cutoff—not memory capacity alone.

NVIDIA RTX 6000 Ada 48GB for local AI
Distinguish the 48GB Ada workstation card from RTX A6000, Quadro RTX 6000 and 96GB RTX PRO 6000 Blackwell before comparing capacity, power or price.

NVIDIA A100 40GB vs 80GB for local AI
Distinguish PCIe cards from bare SXM modules, then check model fit, passive server cooling, power, MIG, NVLink and used-card evidence.

NVIDIA H100 PCIe vs SXM vs H100 NVL for local AI
Identify the exact 80GB or 94GB Hopper variant before comparing capacity, cooling, power, MIG, multi-GPU behavior or used-market offers.

NVIDIA H200 SXM vs H200 NVL for local AI
Evaluate the 141GB HBM3e tier through exact form factor, qualified platform, cooling, 600–700W power ceiling, MIG and whole-system cost.

NVIDIA H200 vs B200 for local AI
Compare 141GB Hopper and 180GB Blackwell per GPU while keeping HGX platform, CUDA generation, MIG and non-pooled multi-GPU memory visible.

NVIDIA L40S vs RTX 6000 Ada for local AI
Compare two 48GB ECC Ada cards through passive server versus active workstation cooling, 350W versus 300W power and system role.

48GB vs 80GB VRAM for local AI
Decide whether the extra 32GB solves a measured model, context or runtime constraint before accepting the deployment cost of an 80GB data-center accelerator.

Best 48GB GPU for local AI? Five paths compared
Choose among A40, RTX A6000, RTX 6000 Ada, L40S and Radeon PRO W7900 using software support, workstation/server cooling, power and measured workload fit.

NVIDIA A40 48GB for local AI
Evaluate this passive Ampere server card through model fit, CPU 8-pin power, default display behavior, CUDA support, NVLink and used-card evidence.

NVIDIA A40 vs RTX A6000 for local AI
Compare two 48GB, 300W Ampere CUDA cards through passive server versus active workstation cooling, displays, bandwidth and NVLink.

NVIDIA A40 vs L40S for local AI
Compare two passive 48GB server cards across Ampere versus Ada, power connectors, bandwidth, NVLink, media engines and software support.

NVIDIA H100 vs H200 for local AI
Compare the Hopper 80GB or 94GB tier with 141GB HBM3e while keeping model fit, memory bandwidth, power, SXM/NVL deployment and whole-system cost separate.

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Best GPU for a 70B LLM? Memory-first paths compared
Shortlist 48GB through 141GB dedicated and unified-memory systems using the exact artifact, KV cache, software stack and deployment constraints rather than a universal speed ranking.

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NVIDIA A100 vs H100 for local AI
Hold 80GB capacity constant while comparing Ampere and Hopper software, bandwidth, power, PCIe/SXM form factors, MIG and multi-GPU behavior.

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How much VRAM do you need for a 32B LLM?
Compare 16GB through 64GB tiers using official Qwen2.5-32B GGUF sizes, explicit KV-cache arithmetic, context and runtime headroom.

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RTX 5090 vs RTX 6000 Ada for local AI
Compare newer 32GB Blackwell with a 48GB ECC Ada workstation GPU using model fit, published bandwidth, 575W-versus-300W power and software support.

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RTX 5090 vs RTX PRO 6000 Blackwell for local AI
Compare same-generation 32GB and 96GB workstation paths with equal published bandwidth but different ECC, MIG, support and workload-fit ceilings.

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RTX PRO 6000 Blackwell vs NVIDIA H100 for local AI
Compare a display-capable 96GB workstation card with 80GB/94GB Hopper data-center variants across memory fit, bandwidth, MIG and complete platform.

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Best GPU for Qwen3-30B-A3B or a 32B LLM?
Shortlist practical consumer, professional and unified-memory options using official artifact sizes, cache headroom and complete-system constraints.

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How much VRAM do you need for a 14B LLM?
Compare 8GB through 24GB tiers using official Qwen2.5-14B GGUF sizes, explicit KV-cache arithmetic, context and runtime headroom.

RTX 4060 Ti 16GB vs RTX 5060 Ti 16GB
Hold capacity constant while comparing Ada and Blackwell software support, power, partner-card design and measured workload behavior.

RTX 5070 Ti 16GB for local AI
Evaluate the higher-power 16GB Blackwell path through exact model fit, capability 12.0 software support, partner-card integration and measured workload evidence.

RTX 5060 Ti 16GB vs RTX 5070 Ti 16GB
Hold capacity and Blackwell software generation constant while comparing memory subsystem, measured workload behavior, 180W-versus-300W reference power and complete offer value.

RTX 5080 16GB for local AI
Evaluate the high-throughput 16GB Blackwell path through exact model fit, capability 12.0 software support, 360W reference power, physical integration and larger-memory alternatives.

RTX 5070 Ti 16GB vs RTX 5080 16GB
Hold capacity and Blackwell software generation constant while comparing measured workload behavior, 896-versus-960GB/s published bandwidth, 300W-versus-360W reference power and complete offer value.

Best 16GB GPU for local AI? Four RTX paths compared
Choose among RTX 4060 Ti, 5060 Ti, 5070 Ti and 5080 with one capacity-first framework covering software support, measured workload behavior, power, partner-card requirements and total cost.

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Best GPU for a 14B LLM?
Compare 12GB, 16GB and 24GB buying paths using official artifact sizes, complete-workload headroom and reproducible evidence.

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Best GPU for a 7B or 8B LLM?
Compare 8GB, 12GB and 16GB buying paths using official artifact sizes, complete-workload headroom and reproducible evidence.

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How much VRAM do you need for an 8B LLM?
Use official Qwen3-8B artifact sizes and explicit cache arithmetic to compare 6GB-through-16GB paths.

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How much VRAM do you need for a 4B LLM?
Use official Qwen3-4B files and explicit cache arithmetic to compare 4GB-through-12GB paths.

What size LLM can I run on my GPU?
Map 4GB through 64GB+ to sourced 4B, 7B, 8B, 14B, 32B and 70B starting points, then calculate the exact workload.

Can my PC run a local LLM?
Check your exact model, available memory, software support and a representative task before buying hardware.

13B vs 14B LLM VRAM requirements
Compare official Meta and Qwen files, raw precision math and architecture-specific KV-cache requirements.

27B vs 32B LLM VRAM requirements
Compare Google’s official Gemma 3 QAT artifact with sourced Qwen files, hybrid-cache limits and multimodal overhead.

MoE LLM VRAM requirements
Understand total versus active parameters with official Qwen3-30B-A3B and gpt-oss memory examples.

gpt-oss-20b vs 120b VRAM requirements
Compare OpenAI’s official MXFP4 and bfloat16 memory guidance, hardware support and multi-GPU constraints.

Qwen3-30B-A3B VRAM requirements
Map Qwen’s official 18.6GB through 32.5GB files and architecture-aware cache arithmetic to 16GB–48GB memory tiers.

New model guide: Gemma 3 27B VRAM requirements compares 16GB, 24GB, 32GB and 48GB paths using Google’s official QAT Q4_0 files.

New family guide: Gemma 3 VRAM requirements compares Google’s official 1B, 4B, 12B and 27B QAT files, vision projectors and practical memory tiers.

New exact-model guide: Gemma 3 12B VRAM requirements compares 8GB, 12GB, 16GB and 24GB using Google’s official QAT files.

New exact-model guide: Gemma 3 4B VRAM requirements compares 4GB, 6GB, 8GB and 12GB using Google’s official QAT files.

New low-memory guide: Gemma 3 1B VRAM requirements covers Google’s official 1GB QAT model, 32K context and CPU or shared-memory deployment.

New exact-model guide: Phi-4 14B VRAM requirements map Microsoft’s official GGUF files from Q2_K through BF16 to practical memory tiers.

New reasoning-model guide: Magistral Small 1.2 24B VRAM requirements map Mistral’s official Q4, Q5, Q8 and BF16 files to practical memory tiers.

Agentic coding: use the Devstral Small 1.1 24B VRAM guide to plan both model memory and the separate host resources used by tools, containers and repositories.

Mixtral 8x7B: compare 24GB through 128GB memory paths using the official checkpoint, labeled community quantizations and exact cache geometry.

Llama 3.2 Vision: plan 11B multimodal VRAM across 24GB, 32GB and 48GB tiers using Meta’s official checkpoint and exact cache geometry.

Llama 3.2 90B Vision: plan 64GB through 256GB multimodal hardware from Meta’s official checkpoint and architecture.

Llama 3.2 family: use the 1B-to-90B Vision VRAM hub for official checkpoint totals, exact cache arithmetic and model-specific hardware paths.

Mistral Small 3.2: plan 16GB through 64GB multimodal paths from the official 48.023GB checkpoint and exact cache geometry.

Mistral 24B comparison: choose among Small 3.2, Devstral and Magistral using modality, runtime and workload evidence—not file size alone.

Vision-model hub: compare Gemma 3, Llama 3.2 Vision and Mistral Small 3.2 VRAM from complete first-party artifact paths.

Qwen vision guide: plan Qwen2.5-VL 7B VRAM across 8GB, 12GB, 16GB and 24GB using complete language-plus-vision artifacts.

Qwen2.5-VL family: compare 3B, 7B, 32B and 72B VRAM requirements using official checkpoints and complete vision workloads.

Qwen2.5-VL 32B: compare 24GB through 96GB paths using complete projector files and 256KiB-per-token cache math.

Qwen2.5-VL 3B: compare 4GB through 12GB paths using complete projector files and 36KiB-per-token cache math.

Qwen2.5-VL 72B: compare 48GB through 192GB paths using complete projector files, 320KiB-per-token cache math and multi-device checks.

Qwen3-VL 8B: plan 8GB through 32GB paths using official Qwen GGUF-plus-projector files and 144KiB-per-token cache math.

Qwen3-VL 32B: plan 24GB through 96GB paths using complete official Q4/Q8 projector pairs and 256KiB-per-token cache math.

Qwen3-VL family: compare 2B, 4B, 8B and 32B with complete official projector paths and model-specific cache math.

Qwen3-VL 4B: plan 4GB through 16GB paths using complete official projector pairs and 144KiB-per-token cache math.

Qwen3-VL 2B: plan 4GB, 6GB and 8GB paths using complete official projector pairs and 112KiB-per-token cache math.

Qwen3-VL 30B-A3B: plan 24GB through 80GB paths without mistaking active routed parameters for stored-weight memory.

Qwen3-VL 235B-A22B: plan exact official split files across 160GB through 640GB without mistaking A22B for stored-weight memory.

Qwen3-VL MoE family: compare 30B-A3B and 235B-A22B using exact official totals, cache math and active-versus-stored safeguards.

Qwen3.5-27B: plan 24GB through 80GB using official BF16 evidence, labeled community projector paths and hybrid-attention safeguards.

Qwen3.5-35B-A3B: plan 24GB through 96GB with official GPTQ/BF16 totals, labeled community GGUF paths and hybrid-MoE safeguards.

Qwen3.5-9B: plan 8GB through 32GB with official BF16 evidence, labeled community Q4/Q8 projector paths and hybrid-attention safeguards.

Qwen3.5-4B: plan 4GB through 16GB with official BF16 evidence, labeled community Q4/Q8 projector paths and hybrid-attention safeguards.

Qwen3.5-2B: plan 2GB through 8GB with official BF16 evidence, labeled community Q4/Q8 projector paths and hybrid-attention safeguards.

Qwen3.5-0.8B: plan 1GB through 4GB with official BF16 evidence, labeled community Q4/Q8 projector paths and edge-device safeguards.

Qwen3.5 family: compare 0.8B through 35B-A3B using complete file totals, practical 1GB–96GB tiers and hybrid-attention safeguards.

Qwen3.5-122B-A10B: plan exact official GPTQ, FP8 and BF16 checkpoints across 80GB through 320GB without mistaking A10B for stored-weight size.

Qwen3.5-397B-A17B: plan exact official GPTQ, FP8 and BF16 checkpoints across 256GB through 1TB without mistaking A17B for stored-weight size.

Best hardware for Qwen3.5 122B and 397B: compare 96GB workstation, 141GB/192GB datacenter, 512GB unified-memory and 1TB-class capacity paths.

AMD Instinct MI300X 192GB for local AI: compare single- and multi-accelerator Qwen3.5 capacity with official ROCm and platform safeguards.

Mac Studio M5 Ultra 512GB for local AI: compare 96GB, 256GB and 512GB unified-memory capacity using exact Qwen3.5 artifacts and MLX safeguards.

Local AI GPU comparisons: browse the consumer, workstation, DGX Spark and data-center comparison clusters from one evidence-first directory.

Best GPU for a 24B LLM: compare 16GB, 24GB, 32GB, 48GB and 64GB+ paths using exact Mistral-class artifact boundaries.

Buying for a 27B model? Compare exact Gemma 3 and Qwen3.5 evidence in the best GPU for a 27B LLM guide.

Choosing a 24GB GPU rather than a model class? Compare RTX 3090 and RTX 4090 for local AI by capacity, evidence quality, power, delivered cost and used-card risk.

New 32GB decision guide: compare RTX 5090 and RTX 5000 Ada for local AI, then calculate fit or build a controlled benchmark record.

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New 12GB decision guide: compare RTX 3060 and RTX 5070 for local AI, then calculate fit or build a controlled benchmark record.

New 8GB buyer guide: RTX 4060 vs RTX 5060 for local AI compares the shared capacity ceiling, exact Qwen3.5-4B boundaries and 12GB/16GB upgrade paths.

New product guide: evaluate RTX 4060 8GB for local AI using exact file fit, Ada compatibility, power and 12GB/16GB alternatives.

New product guide: evaluate RTX 5060 8GB for local AI using exact file fit, Blackwell compatibility, power and 12GB/16GB alternatives.

New product guide: evaluate RTX 5070 12GB for local AI using exact file fit, Blackwell compatibility, power and 16GB alternatives.

New comparison: use RTX 5070 12GB vs RTX 5060 Ti 16GB to decide whether GPU tier or four extra gigabytes matters for the workload.

New same-family comparison: choose between RTX 5060 Ti 8GB and 16GB for local AI using exact artifact boundaries, runtime headroom and controlled-test requirements.

New same-family Ada comparison: choose between RTX 4060 Ti 8GB and 16GB for local AI using exact artifact boundaries, runtime headroom and controlled-test requirements.

New same-family Ampere comparison: choose between RTX 3060 8GB and 12GB for local AI using exact model boundaries, interface differences and used-card checks.

New comparison: RTX 3060 12GB vs RTX 4060 8GB for local AI uses exact file gates and controlled tests to resolve the capacity-versus-generation tradeoff.

New comparison: RTX 3060 12GB vs RTX 5060 8GB for local AI resolves the capacity-versus-Blackwell tradeoff with exact file gates.

RTX 5080 or RTX 5090? Use the 16GB-versus-32GB Blackwell comparison for exact 32B artifact margins, software checks and a controlled purchase test.

RTX 4090 or RTX 5080? Use the 24GB-versus-16GB comparison for exact file-fit boundaries, Blackwell software checks and a controlled purchase test.

New exact comparison: Read the RTX 4090 vs RTX 5090 for local AI for official platform specifications, exact Qwen2.5-32B file-fit boundaries and matched-test controls.

New exact comparison: Read the RTX 3090 vs RTX 5090 for local AI for official platform specifications, exact Qwen2.5-32B file-fit boundaries, used-card safeguards and matched-test controls.

New exact comparison: Read the RTX 3090 vs RTX 5080 for local AI for official specifications, exact 14B and 32B file-fit boundaries, used-card safeguards and matched-test controls.

New AMD consumer guide: Read the RX 7900 XTX 24GB local-AI guide for capacity math, current ROCm checks and a reproducible evaluation plan.

New 24GB cross-platform guide: Compare RX 7900 XTX and RTX 3090 for local AI using exact file-fit arithmetic, current software checks, used-card safeguards and matched-test controls.

New 24GB flagship guide: Compare RX 7900 XTX and RTX 4090 for local AI using exact file-fit arithmetic, current software checks, system safeguards and matched-test controls.

Comparing 24GB Radeon against 32GB Blackwell? Use the RX 7900 XTX vs RTX 5090 local-AI comparison for exact 32B file margins, ROCm/CUDA checks, power planning and matched benchmark controls.

Choosing between 24GB Radeon capacity and a 16GB Blackwell CUDA path? Use the RX 7900 XTX vs RTX 5080 local-AI comparison for exact 32B file margins, software checks, power planning and matched benchmark controls.

Choosing between 24GB Radeon capacity and a 16GB Ada CUDA path? Use the RX 7900 XTX vs RTX 4080 SUPER local-AI comparison for exact 32B file margins, software checks and matched benchmark controls.

Choosing between 24GB Radeon capacity and a 16GB Ada CUDA path? Use the RX 7900 XTX vs RTX 4070 Ti SUPER comparison for exact 32B margins, software checks, power and matched-test controls.

Considering Intel Arc Pro for local AI? Use the Best Intel GPU for Local AI guide to compare B50, B60, B65 and B70 across 16GB, 24GB and 32GB capacity, deployment and oneAPI/OpenVINO/SYCL support gates.

Evaluating Intel Arc Pro B60 24GB? Read the Arc Pro B60 local-AI guide for exact Qwen2.5-32B file margins, official board specifications, Intel software gates and a reproducible test checklist.

Evaluating Intel Arc Pro B70 32GB? Read the Arc Pro B70 local-AI guide for exact 32B file margins, official board specifications, current SYCL caveats and output-correctness controls.

New 16GB guide: use the Intel Arc Pro B50 local-AI decision page for exact model margins, official compact-card specifications and a reproducible benchmark starter.

New 32GB guide: use the Intel Arc Pro B65 local-AI decision page for exact model margins, partner-board checks and a reproducible benchmark starter.

New Intel comparison: see Arc Pro B65 versus B70 for local AI for exact 32B margins and a matched-test protocol.

New Intel comparison: see Arc Pro B50 versus B60 for local AI for exact 32B boundaries and a matched-test protocol.

New Intel comparison: see Arc Pro B60 versus B65 for local AI for exact 32B margins and a matched-test protocol.

New 24GB comparison: Arc Pro B60 versus RTX 3090 separates model fit from software support, power and used-card risk.

Same-capacity cross-platform comparison: Compare Intel Arc Pro B70 32GB with GeForce RTX 5090 32GB by software support, power, system requirements and matched workload evidence.

Same-capacity cross-platform comparison: Compare Intel Arc Pro B60 24GB with GeForce RTX 4090 24GB by software support, power, system requirements and matched evidence.

Planning a mixture-of-experts deployment? The Llama 4 Scout and Maverick VRAM guide uses exact official BF16 and FP8 checkpoint sizes to separate active parameters, stored weights, context and runtime headroom.

New model guide: GLM-4.7 and GLM-4.7-Flash VRAM requirements maps exact official BF16 and FP8 files from 62.444GB through 716.681GB onto single- and multi-device capacity boundaries.

New buyer guide: Best hardware for GLM-4.7-Flash starts at the exact 62.444GB BF16 checkpoint and compares 80GB, 96GB and larger single-accelerator paths.

New model guide: DeepSeek-V3.2 VRAM requirements uses the exact 689.483GB official checkpoint to explain why 640GB is insufficient and 768GB is only a lower-bound file fit.