Best GPU for a 27B LLM? 24GB, 32GB, 48GB and 80GB Paths

Test before buying: use the Qwen3.8-27B local deployment guide to record the exact GGUF, runtime, context, placement and peak memory.

Qwen3.8 buyer note: the Qwen3.8-27B checkpoint guide separates community 13.15–29.05GB GGUF options from Qwen’s official 30.87GB FP8 and 55.56GB standard files.

Short answer: for a current 27B local LLM, 24GB is the practical consumer starting tier for selected 4-bit artifacts, 32GB adds valuable context and quantization headroom, 48GB is the practical higher-bit and heavier-workload tier, and 64GB or 80GB is the relevant one-device range for the official 55.563GB Qwen3.5-27B BF16 checkpoint. A 16GB GPU is not a full-device fit for Google’s official 17.2GB Gemma 3 27B QAT Q4_0 file. There is no universal fastest or best-value card: choose by the exact artifact, runtime, context, modality, software support and whole-system cost.

Best 27B GPU paths at a glance

Memory tier	27B planning role	Hardware path to evaluate	Main limitation
16GB	Partial offload or smaller/lower-bit alternatives	Existing 16GB GPU, if you accept a different placement and performance path	Google’s official 17.2GB Gemma 3 27B QAT model file already exceeds the pool
24GB	Practical single-GPU Q4 starting tier	Used RTX 3090 for acquisition cost; RTX 4090 when measured throughput and the complete system justify it	Long context, vision, concurrency or larger quants can consume the remaining margin
32GB	Roomier Q4 and bounded Q8 experiments	RTX 5090 when current CUDA support, power and system fit are acceptable	The 29.523GB Qwen3.5 Q8-plus-projector pair is tight before runtime state
48GB	Practical Q8 and heavier single-device workloads	RTX A6000, RTX 6000 Ada, L40S, A40 or Radeon Pro W7900 after software-path checks	Capacity does not establish speed, support, cooling or total value
64GB–80GB	Official BF16 checkpoint and larger-context/concurrent planning	Large unified-memory system or qualified 80GB accelerator	Platform, cooling, software and whole-system cost dominate the decision

The exact artifact boundaries behind the recommendation

27B example	Artifact evidence	Displayed total	Practical implication
Gemma 3 27B IT	Official Google QAT Q4_0 GGUF	17.2GB model; separate 858MB vision projector	16GB is not a full-device model fit; 24GB is the first common starting tier
Qwen3.5-27B	Community Q4_K_M GGUF + F16 projector	17.668GB combined	24GB is practical with a controlled workload; conversion provenance must be verified
Qwen3.5-27B	Community Q8_0 GGUF + F16 projector	29.523GB combined	32GB is tight; 48GB is the practical tier
Qwen3.5-27B	Official Qwen BF16 safetensors	55.563GB	64GB is bounded; 80GB provides safer headroom

Which GPU path should you choose?

Lowest-cost practical CUDA path: used RTX 3090 24GB

Evaluate a used RTX 3090 when acquisition cost matters and your exact runtime supports Ampere. Its 24GB pool can be a practical home for the listed Q4 examples. Used condition, 350W-class power, cooling, case fit and return terms remain part of the price.

24GB with a newer/faster consumer path: RTX 4090

Evaluate an RTX 4090 when controlled measurements show its throughput benefit matters. It still has 24GB, so buying it does not create more memory headroom than an RTX 3090. Compare the three generations in the RTX 3090 vs 4090 vs 5090 guide.

Best consumer capacity step: RTX 5090 32GB

Evaluate an RTX 5090 when the extra 8GB of dedicated memory, current Blackwell software path and measured performance justify its price, 575W board-power class and system requirements. Thirty-two gigabytes provides useful Q4 headroom but does not make the listed 29.523GB Q8-plus-projector workload comfortable after cache and runtime allocations.

Practical Q8/headroom tier: one 48GB pool

Move to a one-device 48GB path when the workload—not the label—requires materially more space. Compare the 48GB GPU decision guide across workstation, server and AMD options. Check CUDA or ROCm support, card identity, passive-airflow requirements, ECC needs, power, warranty and full delivered system cost.

24GB versus 32GB: the decision that matters most

• Choose 24GB when a verified Q4 artifact, controlled context and one-user workload fit with measured headroom. This is usually the value path.
• Choose 32GB when you want more context/cache margin, multimodal room, adjacent 32B flexibility or less dependence on a tightly packed 24GB configuration.
• Do not choose 32GB solely for Q8. The listed Qwen3.5 Q8-plus-projector files leave only 2.477GB nominally on a 32GB device before cache, hybrid-attention state, runtime buffers, vision work or display use.
• Do not treat two GPUs as one pool. Software may split work, but topology, placement, interconnect and runtime support determine behavior.

Use the 24GB, 32GB and 48GB capacity guide for the broader tier comparison.

Context, vision and runtime can change the answer

• Context: allocated prompt plus output tokens and cache precision affect memory. A model’s maximum context is not a promise that the maximum fits on your card.
• Hybrid attention: both cited model families use architecture-specific cache/state behavior; a naive all-layers KV formula is not an exact total.
• Vision: projector files, image resolution/count, preprocessing and intermediate allocations make text-only and multimodal tests different workloads.
• Concurrency: parallel requests can multiply cache and activation pressure.
• Runtime: kernel support, quant format, GPU offload, fallback and allocator behavior can change both fit and speed.
• Quality: lower-bit fit is not proof that output quality is acceptable for your task.

A purchase test that protects against the wrong upgrade

1. Record the exact model repository, revision, artifact, projector and checksum.
2. Fix the runtime version, context allocation, cache precision, input shape, output target and concurrency.
3. Measure peak accelerator and system memory after load and during the real task.
4. Capture time to first token, prompt processing and generation throughput with clear definitions.
5. Evaluate output quality for the intended coding, RAG, vision or chat workload.
6. Compare whole-system price, power, cooling, noise, warranty and return risk.
7. Only then compare a cheaper 24GB path with a 32GB or 48GB alternative.

Use the repeatable workload template, review the source-reported benchmark database, or submit a controlled 27B result. The database does not currently establish a universal 27B GPU ranking.

Sources and limits

• Google official Gemma 3 27B IT QAT Q4_0 GGUF file tree
• Google Gemma 3 model card
• Qwen official Qwen3.5-27B repository
• Qwen official BF16 checkpoint index
• Unsloth community Qwen3.5-27B GGUF conversion
• HighVRAM methodology

Reviewed October 25, 2026. File sizes and architecture boundaries are tied to the linked repositories. HighVRAM has not independently benchmarked every GPU, runtime or artifact on this page. Product inclusion is a decision path, not an endorsement or performance ranking. Check current price, condition, compatibility and availability before buying.

Buying at the 32GB tier? Compare RTX 5090 and RTX 5000 Ada for local AI without confusing generation, ECC or bandwidth with a larger capacity ceiling.