32 GB VRAM (+8)1792 GB/s (+1120)
B
Raises estimated decode speed by about 112%.82 tok/s decode
Raises estimated decode speed by about 112%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Mistral Small 24B Instruct 2501 run on RTX PRO 4000 Blackwell 24GB?
YES — Tight Fit
C51Usable
○Estimated from fit model
Mistral Small 24B Instruct 2501 needs ~21.1 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 21.1 GB, 38.6 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
38.6 tok/s
TTFT
5021 ms
Safe context
33K
Memory
21.1 GB / 24.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsMistral Small 24B Instruct 2501 on RTX PRO 4000 Blackwell 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 38.6 tok/s decode · 5.0s TTFT (warm) · 96 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 38.6 tok/s | 2739 ms | 33K |
| Coding | C | Tight fit | 38.6 tok/s | 5021 ms | 33K |
| Agentic Coding | C | Runs with offload | 38.6 tok/s | 7303 ms | 33K |
| Reasoning | C | Tight fit | 38.6 tok/s | 5934 ms | 33K |
| RAG | C | Runs with offload | 38.6 tok/s | 9129 ms | 33K |
Quantization options
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server startUpgrade options
Hardware that runs Mistral Small 24B Instruct 2501 well
32 GB VRAM (+8)896 GB/s (+224)
C
Raises estimated decode speed by about 33%.51.4 tok/s decode
Raises estimated decode speed by about 33%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP