Advisor / Models / Mistral Small 3.2 24B

Mistral Small 3.2 24B: VRAM requirements & speed

Mistral Small 3.2 24B (24.01B dense, apache-2.0). Native context 128k. Smallest GGUF: 14.33 GB.

14.33 GBsmallest quant file
18hardware pages that fit it fully
7run with offload
0measured data points

Memory needed by quantization and context

GGUF file size plus f16 KV cache (and fixed overhead of ~1 GB per GPU not shown). Source: unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF.

QuantFile+8k ctx+32k ctx+128k ctx
Q4_K_M14.33 GB15.7 GB19.7 GB35.8 GB
Q5_K_M16.76 GB18.1 GB22.1 GB38.2 GB
Q6_K19.35 GB20.7 GB24.7 GB40.8 GB
Q8_025.05 GB26.4 GB30.4 GB46.5 GB

KV cache size by context length

Computed from the model's attention layout.

Contextf16q8_0q4_0
8k1.34 GB0.71 GB0.38 GB
32k5.37 GB2.85 GB1.51 GB
128k21.47 GB11.41 GB6.04 GB

Which hardware runs Mistral Small 3.2 24B? · 16k context

HardwareQuantFitMemoryTok/sSpeed source
RTX 5090 32GBQ8_0fits fully27.7 GB56scaled from measured
RTX PRO 6000 96GB (bonus page)Q8_0fits fully27.7 GB44estimated
RTX 4090 24GBQ6_Kfits fully22.0 GB42scaled from measured
RTX 3090 24GBQ6_Kfits fully22.0 GB37scaled from measured
AMD RX 7900 XTX 24GBQ6_Kfits fully22.0 GB30scaled from measured
Mac Studio M5 Ultra 256GBQ8_0fits fully27.7 GB30estimated
Mac Studio M5 Ultra 512GB (ships late Oct 2026)Q8_0fits fully27.7 GB30estimated
2x RTX 3090 (48GB total)Q8_0fits fully27.7 GB26estimated
RTX 5080 16GBQ4_K_Mpartial offload (slow)17.0 GB20estimated
Mac Studio M5 Max 128GBQ8_0fits fully27.7 GB20scaled from measured
MacBook Pro 14/16 M5 Max 128GBQ8_0fits fully27.7 GB20scaled from measured
RTX 5070 Ti 16GBQ4_K_Mpartial offload (slow)17.0 GB19estimated
Mac Studio (2025) M3 Ultra 96GBQ6_Kfits fully22.0 GB17estimated
MacBook Pro 14/16 (2024) M4 Max 128GBQ5_K_Mfits fully19.4 GB15estimated
MacBook Pro 14/16 M5 Pro 48GBQ4_K_Mfits fully17.0 GB15scaled from measured
AMD Ryzen AI Max+ 395 (Strix Halo) 128GBQ4_K_Mfits fully17.0 GB14scaled from measured
16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)Q4_K_Mpartial offload (slow)17.0 GB13estimated
NVIDIA DGX Spark 128GBQ4_K_Mfits fully17.0 GB11scaled from measured
Mac mini (2024) M4 Pro 64GBQ4_K_Mfits fully17.0 GB11scaled from measured
RTX 5070 12GBQ4_K_Mpartial offload (slow)17.0 GB9estimated
RTX 4070 12GBQ4_K_Mpartial offload (slow)17.0 GB8estimated
Mac mini M6 32GBQ4_K_Mfits fully17.0 GB7estimated
MacBook Air M5 32GBQ4_K_Mfits fully17.0 GB6scaled from measured
8GB VRAM class (RTX 4060 8GB, 32GB RAM)Q4_K_Mpartial offload (slow)17.0 GB5estimated
RTX 3060 12GB (32GB RAM)Q4_K_Mpartial offload (slow)17.0 GB5estimated
Mac mini / iMac M4 16GB-does not fit--

Discrete GPUs are modelled with 32 GB of system RAM available for offload. Does not fit even with offload: Mac mini / iMac M4 16GB.

Links

Model card · GGUF files · Open in the advisor · last verified 2026-10-14

FAQ

How much VRAM does Mistral Small 3.2 24B need?

The smallest quant (Q4_K_M) is 14.33 GB, so you need roughly 17 GB for an 8k context including KV cache and overhead. At 128k context the f16 KV cache alone is 21.5 GB.

Can I run Mistral Small 3.2 24B on a RTX 5090 32GB?

Yes: on RTX 5090 32GB it fits fully as Q8_0 at about 56 tok/s (scaled from measured). 18 of 26 hardware configurations fit it fully at 16k context.

What is the maximum context for Mistral Small 3.2 24B?

128k tokens natively. Memory, not the model, is usually the limit: see the KV cache table above.

All models: model index · by memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB. Numbers use the same fit math as the advisor; sources and methods.