Llama 3.2 11B Vision VRAM: 24GB, 32GB or 48GB?

Short answer: Meta’s official Llama 3.2 11B Vision Instruct checkpoint contains 21.34GB across five BF16 safetensor shards. That makes 24GB a file-size trap for the official checkpoint, 32GB a practical shorter-context BF16 starting tier, and 48GB the safer BF16 tier for larger text/image workloads. Community GGUF conversions can be much smaller—one repository lists a 5.96GB Q4_K_M and 10.4GB Q8_0 model file—but vision-projector packaging differs, so never budget from the language-model file alone.

Official Llama 3.2 11B Vision checkpoint size

Official shard	Bytes	Decimal GB
model-00001-of-00005.safetensors	4,992,622,346	4.993GB
model-00002-of-00005.safetensors	4,966,251,712	4.966GB
model-00003-of-00005.safetensors	4,915,919,704	4.916GB
model-00004-of-00005.safetensors	4,999,823,980	5.000GB
model-00005-of-00005.safetensors	1,465,943,128	1.466GB
Total	21,340,560,870	21.341GB

24GB, 32GB or 48GB?

• 24GB: the official 21.34GB checkpoint leaves only about 2.66GB on the decimal labels before KV cache, vision workspaces, runtime buffers and display use. Treat it as a file-size trap, not a comfortable BF16 fit.
• 32GB: a practical BF16 starting tier for carefully bounded text/image workloads. An illustrative 8K FP16/BF16 text cache is 8GiB before vision-specific and runtime allocations, so validate the exact prompt and image path.
• 48GB: adds useful room for BF16 weights, larger text allocation, images and runtime state. It is still not proof that the architectural 128K maximum fits.
• Quantized paths: 8GB, 12GB, 16GB and 24GB may be feasible with a compatible conversion, but add every required vision component and cache allocation before choosing hardware.

Compare the 24GB/32GB/48GB tiers and use the VRAM calculator as a starting range rather than a multimodal guarantee.

Why the vision model is not just an 11B text model

Meta describes Llama 3.2 Vision as a multimodal model family with 11B and 90B variants, text-and-image input, and text output. Meta’s architecture listing for the 11B instruct model records a 4,096-dimensional, 32-layer language model with 32 attention heads, 8 KV heads, 8 vision cross-attention layers, a 560-pixel vision chunk size and up to 4 chunks. Those vision components and image-derived tokens are part of the workload.

Do not compare the 11B Vision checkpoint directly with a text-only 13B/14B sizing estimate and assume the smaller parameter label guarantees lower peak memory. Architecture and modality matter.

Text KV-cache arithmetic

Meta’s official architecture listing records 32 layers, 8 KV heads, 32 attention heads and a 4,096 hidden size. The head dimension is 4,096 ÷ 32 = 128. For one sequence with a two-byte FP16/BF16 cache and every language layer on the accelerator:

bytes per token = 2 × 32 layers × 8 KV heads × 128 head dimension × 2 bytes
                = 1,048,576 bytes
                = 1 MiB per token

Allocated text-token slots	Illustrative FP16/BF16 KV payload
4,096	4GiB
8,192	8GiB
16,384	16GiB
32,768	32GiB
131,072	128GiB

Meta lists a 128K architectural context length for Llama 3.2 Vision. The table shows why that number is not a consumer-GPU fit promise.

Community GGUF files need a component audit

Meta does not publish the GGUF examples used here. One community conversion lists 5.96GB Q4_K_M, 10.4GB Q8_0 and 19.6GB FP16 model files. Another community repository exposes a separate 1.94GB FP16 multimodal projector. These are useful examples of why two repositories can present different-looking totals.

• Record the exact repository, revision, main model file and checksum.
• Determine whether the vision projector is integrated or separate.
• Confirm the runtime actually supports the model architecture and image path.
• Add both components plus cache and runtime allocations before declaring a fit.

Use the quantization guide without treating a file-size reduction as proof of unchanged vision or language quality.

A reproducible multimodal fit test

1. Record exact model repository, revision, license acceptance, files and checksums.
2. Record whether the projector is integrated or separate and its precision.
3. Record runtime/version, accelerator placement and any CPU offload.
4. Record GPU or unified-memory capacity, system RAM, OS and driver.
5. Record image dimensions, chunk count, number of images, text tokens, output target, concurrency and cache precision.
6. Measure available memory before load, after load, after image encoding and at peak generation.
7. Capture time to first token, throughput, preprocessing time and any fallback or out-of-memory error.
8. Evaluate image-grounded answer quality; successful loading is not proof that vision works correctly.

Run the same disclosed workload and share a reproducible result.

Official sources and limits

• Meta official gated model repository
• Meta Llama model list and context lengths
• Meta official architecture listing
• Community integrated-file GGUF example
• Community separate-projector example
• HighVRAM methodology

Reviewed October 25, 2026. Official shard sizes and architecture fields come from Meta repositories; community GGUF examples are labeled separately. HighVRAM has not independently benchmarked every file, image configuration or runtime. Fit does not establish speed or output quality.

Need the larger model? The Llama 3.2 90B Vision VRAM guide maps Meta’s official 177.187GB checkpoint and exact cache costs to 64GB-through-256GB deployment paths.

Compare the whole family: the Llama 3.2 VRAM hub places this 11B Vision checkpoint beside the official 1B, 3B and 90B Vision variants with exact cache geometry.