32 GB VRAM (+8)1792 GB/s (+1492)
B
Raises estimated decode speed by about 160%.112 tok/s decode
Raises estimated decode speed by about 160%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Ministral 8B run on NVIDIA L4 24GB?
YES — Runs Great
B58Good
○Estimated from fit model
Ministral 8B needs ~10.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.7 GB, 43.0 tok/s, Runs well
10.7 GB required24.0 GB available
45% VRAM used
Fit status
Runs well
Decode
43.0 tok/s
TTFT
4507 ms
Safe context
113K
Memory
10.7 GB / 24.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsMinistral 8B on NVIDIA L4 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: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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 | B | Runs well | 43.0 tok/s | 2458 ms | 113K |
| Coding | B | Runs well | 43.0 tok/s | 4507 ms | 113K |
| Agentic Coding | B | Runs well | 43.0 tok/s | 6556 ms | 113K |
| Reasoning | B | Runs well | 43.0 tok/s | 5326 ms | 113K |
| RAG | B | Runs well | 43.0 tok/s | 8194 ms | 113K |
Quantization options
How Ministral 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 | 4.5 GB | Medium | C55 |
Q4_K_M | 4 | 4.9 GB | Medium | C55 |
Q5_K_M | 5 | 5.8 GB | High | B55 |
Q6_K | 6 | 6.6 GB | High | B56 |
Q8_0 | 8 | 8.6 GB | Very High | B57 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | B59 |
Get started
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
Hardware that runs Ministral 8B well
32 GB VRAM (+8)896 GB/s (+596)
B
Raises estimated decode speed by about 160%.112 tok/s decode
Raises estimated decode speed by about 160%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+276)
B
Raises estimated decode speed by about 136%.101.5 tok/s decode
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP