Multimodal LLM VRAM: Gemma 3 vs Llama 3.2 Vision vs Mistral

Exploring the 512GB Apple-silicon path? See whether a 512GB Mac Studio can run Muse Spark Open, including the published 304GB Q2_K and 344GB oQ3e tests and why they do not replace the official 700GB NVFP4 requirement.

New 700GB-class workload: the Muse Spark Open VRAM guide separates its exact 533.011GB NVFP4 files from Meta’s official 700GB minimum and compares 640GB, 705GB, 720GB and 768GB pools.

Large multimodal example: the Kimi-K2.5 VRAM guide separates its 595.178GB static checkpoint from vision and live-runtime memory.

Short answer: practical local vision-model memory ranges from about 6GB for Gemma 3 4B to 256GB for official Llama 3.2 90B Vision BF16. Between them, Gemma 3 12B targets 12GB/16GB, Gemma 3 27B targets 24GB, Llama 3.2 11B Vision targets 32GB/48GB for official BF16, and Mistral Small 3.2 24B targets 64GB for official BF16. The exact answer must include language weights, vision encoder or projector, text cache, image processing, runtime buffers and concurrency.

Local vision LLM VRAM at a glance

Model path	First-party artifact evidence	Vision packaging	Practical starting tier
Gemma 3 4B IT QAT Q4_0	3.16GB model + 851MB projector	Separate projector	6GB; 4GB is constrained
Gemma 3 12B IT QAT Q4_0	8.07GB model + 854MB projector	Separate projector	12GB; 16GB adds headroom
Gemma 3 27B IT QAT Q4_0	17.2GB model + 858MB projector	Separate projector	24GB; 16GB is below combined files
Llama 3.2 11B Vision Instruct BF16	21.341GB official checkpoint	Integrated official multimodal checkpoint; community packaging varies	32GB bounded; 48GB safer
Mistral Small 3.2 24B Instruct BF16	48.023GB official checkpoint	Integrated official Mistral3/Pixtral checkpoint; community packaging varies	64GB
Llama 3.2 90B Vision Instruct BF16	177.187GB official checkpoint	Integrated official multimodal checkpoint; community packaging varies	192GB constrained; 256GB safer

Three packaging patterns to recognize

• Separate model and projector: the official Gemma 3 QAT GGUF path presents a language-model file plus a roughly 851–858MB multimodal projector. Add both.
• Integrated official checkpoint: Meta’s Llama 3.2 Vision and Mistral Small 3.2 repositories include the multimodal architecture in their official safetensor checkpoints.
• Community conversion: GGUF repositories may integrate, separate or omit vision components. Inspect the exact files and runtime instructions rather than assuming one convention.

Gemma 3: efficient official QAT paths

Google’s official Gemma 3 QAT files provide unusually direct small- and mid-size multimodal evidence. The separate projector makes the complete-file arithmetic visible: about 4.01GB for 4B, 8.92GB for 12B and 18.06GB for 27B before cache and runtime state.

Use the Gemma 3 family VRAM guide for the 1B text-only distinction, exact 4B/12B/27B files, context geometry and per-model routes.

Qwen2.5-VL 7B: compact, but the projector still counts

Qwen’s official integrated BF16 checkpoint totals 16.584GB. The ggml-org community GGUF route separates the language file from an 853MB Q8_0 or 1.354GB F16 multimodal projector: a 4.683GB Q4_K_M file is therefore not the complete vision workload. The Qwen2.5-VL 7B VRAM guide maps complete files, cache and image/video inputs to constrained 8GB, practical 12GB/16GB and BF16 24GB paths.

Llama 3.2 Vision: cache geometry changes the answer

Meta’s official checkpoints occupy 21.341GB for 11B Vision and 177.187GB for 90B Vision. Counterintuitively, their language-cache cost per token is not proportional to parameter count: the official architecture yields 1MiB/token for 11B Vision and 320KiB/token for 90B Vision.

Start with the Llama 3.2 family guide, then open the exact 11B Vision or 90B Vision guide for shard totals, cache tables and image-path checks.

Mistral Small 3.2: multimodal 24B with a 48GB trap

Mistral’s official checkpoint is 48.023GB whether downloaded as one consolidated file or the alternative ten-shard representation. Do not sum both. The official configuration adds a Pixtral vision encoder with 1,540-pixel images, 14-pixel patches, 24 vision layers and a 1,024-dimensional vision hidden state.

The Mistral Small 3.2 VRAM guide maps community Q4–Q8 files to 16GB–32GB paths and the official BF16 checkpoint to 64GB while preserving projector and runtime provenance.

How image inputs change memory

• Resolution and tiling/chunking: larger images or more tiles produce more vision work and image-derived tokens.
• Image count: multiple images can increase preprocessing, encoder state and prompt length.
• Vision precision: an F16 projector or encoder has a different footprint from a quantized component.
• Cross-attention: image representations must interact with language layers according to the model architecture.
• Runtime buffers: kernels and workspaces can peak during image encoding or generation, not only at model load.
• Unified memory: the operating system and applications share the physical pool; the label is not fully available to the model.

A reproducible vision-model fit test

1. Record exact model repository, revision, license, every required file, size and checksum.
2. State whether the projector/vision encoder is integrated, separate or omitted.
3. Record model, projector, vision and cache precision.
4. Record runtime/version, accelerator placement, system RAM, OS and driver.
5. Record image dimensions, tiles/chunks, image count, text-token allocation, output target and concurrency.
6. Measure memory before load, after load, after image encoding and at peak generation.
7. Capture preprocessing time, time to first token, throughput, fallback and out-of-memory errors.
8. Test image-grounded correctness with examples whose answers require inspecting the image.

Use the VRAM calculator as a starting range, compare VRAM with unified memory, run the same disclosed workload, and share a reproducible result.

Qwen family route: compare official 3B, 7B, 32B and 72B checkpoints, cache geometry and multimodal tiers in the Qwen2.5-VL VRAM family guide.

Upper-tier Qwen path: the Qwen2.5-VL 32B VRAM guide adds complete Q4/Q8 projector pairs, 256KiB/token cache math and 24GB–96GB decisions.

Compact Qwen path: the Qwen2.5-VL 3B VRAM guide adds complete Q4/Q8 projector pairs, 36KiB/token cache math and 4GB–12GB decisions.

Large Qwen path: the Qwen2.5-VL 72B VRAM guide adds complete Q4/Q8 projector pairs, 320KiB/token cache math and 48GB–192GB decisions.

Current Qwen vision path: the Qwen3-VL 8B VRAM guide uses official Qwen GGUF-plus-projector files, 144KiB/token cache math and native 256K architecture.

Current high-capacity Qwen path: the Qwen3-VL 32B VRAM guide combines official language and projector files with 256KiB-per-token cache arithmetic.

Current Qwen family: the Qwen3-VL VRAM hub compares official 2B, 4B, 8B and 32B language-plus-projector paths.

Compact current-generation path: the Qwen3-VL 4B VRAM guide uses complete official model-plus-projector totals and 144KiB-per-token cache math.

Smallest current-generation path: the Qwen3-VL 2B VRAM guide uses complete official model-plus-projector totals and 112KiB-per-token cache math.

Current MoE vision path: the Qwen3-VL 30B-A3B guide combines official language and projector files with architecture-specific cache math.

Flagship MoE vision path: the Qwen3-VL 235B-A22B guide combines exact split-file sums, projector memory and multi-device checks.

Qwen MoE branch: the Qwen3-VL 30B-A3B vs 235B-A22B hub combines complete projector paths, model-specific cache and multi-device checks.

Sources and limits

• Google official Gemma 3 QAT collection
• Meta official Llama 3.2 11B Vision repository
• Meta official Llama 3.2 90B Vision repository
• Mistral official Small 3.2 repository
• HighVRAM methodology

Reviewed October 25, 2026. Artifact evidence comes from official Google, Meta and Mistral repositories. HighVRAM has not independently benchmarked every model, conversion, image configuration or runtime. This is a capacity guide, not a universal performance ranking.

New hybrid-attention path: the Qwen3.5-27B guide separates official checkpoint/config evidence from community conversion files and keeps recurrent state explicit.

Hybrid MoE vision path: the Qwen3.5-35B-A3B guide keeps projector, expert storage and implementation-dependent linear state explicit.

Mainstream hybrid-attention path: the Qwen3.5-9B guide keeps community projector files, full-attention cache and implementation-dependent linear state separate.

Compact hybrid-attention path: the Qwen3.5-4B guide keeps community projector files, full-attention cache and implementation-dependent linear state separate.

Small hybrid-attention path: the Qwen3.5-2B guide keeps community projector files, full-attention cache and implementation-dependent linear state separate.

Edge hybrid-attention path: the Qwen3.5-0.8B guide keeps community projector files, full-attention cache and implementation-dependent linear state separate.

Current hybrid-attention family: the Qwen3.5 VRAM hub compares six natively multimodal sizes, projector-inclusive community paths and first-party BF16 checkpoints.

High-capacity hybrid MoE vision path: the Qwen3.5-122B-A10B guide separates first-party GPTQ/FP8/BF16 from community projector-inclusive GGUF deployments.

Flagship multimodal MoE: plan Qwen3.5-397B-A17B across 256GB-to-1TB systems with projector, vision-workload and hybrid-attention safeguards.

Need Meta’s newer mixture-of-experts family? Continue with the Llama 4 Scout and Maverick VRAM guide for official multimodal context claims, exact checkpoint bytes and distributed file-fit boundaries.