RTX 3090 24GB经济之选24 GB VRAM (+4)936 GB/s (+296)
C
Raises estimated decode speed by about 38%.34.1 tok/s 解码
Raises estimated decode speed by about 38%.
~$1,499 MSRP
Can Mistral Small 3.2 24B Instruct 2506 run on RTX A4500 20GB?
YES — With Offload
C50Usable
Estimated from fit model
Mistral Small 3.2 24B Instruct 2506 needs ~20.4 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~25 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 20.4 GB, 24.7 tok/s, Runs with offload (needs ~0.3 GB host RAM)
20.4 GB required20.0 GB available
102% VRAM needed
0.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
24.7 tok/s
TTFT
7854 ms
Safe context
14K
Memory
20.4 GB / 20.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsMistral Small 3.2 24B Instruct 2506 on RTX A4500 20GB
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: 24.7 tok/s decode · 7.9s TTFT (warm) · 62 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 34.1 tok/s | 3097 ms | 14K |
| Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 24.7 tok/s | 7854 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~2 GB host RAM) | 18.8 tok/s | 15002 ms | 14K |
| Reasoning | C | Runs with offload (needs ~0.3 GB host RAM) | 24.7 tok/s | 9282 ms | 14K |
| RAG | D | Very compromised | 18.8 tok/s | 18752 ms | 14K |
Quantization options
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C51 |
Q3_K_S | 3 | 11.8 GB | Low | C51 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 14.6 GB | Medium | C50 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.
Run
lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server start升级选项
能流畅运行 Mistral Small 3.2 24B Instruct 2506 的硬件
RTX 3090 24GB经济之选RTX PRO 4000 Blackwell 24GB最佳性价比24 GB VRAM (+4)672 GB/s (+32)
C
Raises estimated decode speed by about 56%.38.6 tok/s 解码
Raises estimated decode speed by about 56%.
~$1,599 MSRP
RTX A5500 24GBNVIDIA 升级24 GB VRAM (+4)768 GB/s (+128)
C
Raises estimated decode speed by about 66%.40.9 tok/s 解码
Raises estimated decode speed by about 66%.
~$3,200 MSRP
Frequently asked questions
Can RTX A4500 20GB run Mistral Small 3.2 24B Instruct 2506?
Yes, RTX A4500 20GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 24.7 tok/s.
How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Small 3.2 24B Instruct 2506?
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Small 3.2 24B Instruct 2506 run at on RTX A4500 20GB?
On RTX A4500 20GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 24.7 tokens per second decode speed with a time-to-first-token of 7854ms using Q4_K_M quantization.
Can RTX A4500 20GB run Mistral Small 3.2 24B Instruct 2506 for coding?
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on RTX A4500 20GB receives a C grade with 24.7 tok/s and 14K context.
What context window can Mistral Small 3.2 24B Instruct 2506 use on RTX A4500 20GB?
On RTX A4500 20GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Mistral Small 3.2 24B Instruct 2506 feels slow on RTX A4500 20GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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