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Mistral AI · Dense · GQA

Mistral Small 24B Instruct 2501: VRAM by quantization and context

23.6B parameters, 40 layers, 8 KV heads. Here is what that means in gigabytes.

23.6B parameters 14.2 GB at Q4_K_M 40 layers 8 KV heads
Architecture
Parameters23.6BVendor model card
Layers40config.json
Hidden size5120config.json
Attention heads32config.json
KV heads8config.json (GQA)
Head dim128config.json
Max context32768 tokensconfig.json

Source: mistralai/Mistral-Small-24B-Instruct-2501 config.json

VRAM by quantization
Total includes 8K of KV cache and 1.5 GB runtime overhead.
QuantizationWeightsKV @ 8KTotal neededRuns on
FP16 / BF16
2.0 B/param
47.2 GB1.3 GB50.0 GBMac Studio M4 Max 128GB
INT8 / Q8_0
1.06 B/param
25.0 GB1.3 GB27.9 GBMac Studio M4 Max 128GB
Q4_K_M (4-bit)
0.6 B/param
14.2 GB1.3 GB17.0 GBMac Studio M4 Max 128GB
Q5_K_M (5-bit)
0.72 B/param
17.0 GB1.3 GB19.8 GBMac Studio M4 Max 128GB
KV cache by context length
Q4_K_M weights plus 1.5 GB overhead. KV cache at FP16.
ContextKV cacheTotal neededRuns on
4k0.7 GB16.3 GBMac Studio M4 Max 128GB
8k1.3 GB17.0 GBMac Studio M4 Max 128GB
16k2.7 GB18.3 GBMac Studio M4 Max 128GB
32k5.4 GB21.0 GBMac Studio M4 Max 128GB
64k10.7 GB26.4 GBMac Studio M4 Max 128GB
128k21.5 GB37.1 GBMac Studio M4 Max 128GB

Verdict

At Q4_K_M the weights alone are about 14.2 GB. With 8K of context and 1.5 GB of runtime overhead it lands near 17.0 GB. It fits: RTX 4090, RTX 3090, RTX 5090, Mac Studio M4 Max 128GB.

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All throughput figures are estimates derived from memory bandwidth and published measurements. VRAM figures are computed from model architecture and quantization assumptions. Verify against your own runtime before buying hardware.