Will It Run AI

Can mistral small 3.1 24b instruct 2503 hf run on NVIDIA A16 64GB?

YES — Runs Great

C47Usable
Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~25.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.1 GB, 32.0 tok/s, Runs well
25.1 GB required64.0 GB available
39% VRAM used

Fit status

Runs well

Decode

32.0 tok/s

TTFT

6056 ms

Safe context

238K

Memory

25.1 GB / 64.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on NVIDIA A16 64GB
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: 32.0 tok/s decode · 6.1s TTFT (warm) · 80 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well32.0 tok/s3303 ms238K
CodingCRuns well32.0 tok/s6056 ms238K
Agentic CodingCRuns well32.0 tok/s8809 ms238K
ReasoningCRuns well32.0 tok/s7157 ms238K
RAGCRuns well32.0 tok/s11011 ms238K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC41
Q3_K_S
3
11.8 GB
LowC42
NVFP4
4
13.4 GB
MediumC42
Q4_K_M
4
14.6 GB
MediumC42
Q5_K_M
5
17.3 GB
HighC43
Q6_K
6
19.7 GB
HighC43
Q8_0
8
25.7 GB
Very HighC45
F16Best for your GPU
16
49.2 GB
MaximumC48

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

アップグレードオプション

mistral small 3.1 24b instruct 2503 hfを快適に動かすハードウェア

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視
96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 221%.102.8 tok/sデコード

Raises estimated decode speed by about 221%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBコスパ最良
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 186%.91.6 tok/sデコード

Raises estimated decode speed by about 186%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

NVIDIANVIDIA H20 96GBNVIDIAアップグレード
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 592%.221.3 tok/sデコード

Raises estimated decode speed by about 592%.

Adds memory headroom for longer context windows and future model growth.

〜$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run mistral small 3.1 24b instruct 2503 hf?

Yes, NVIDIA A16 64GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 25.1 GB of memory with Q4_K_M quantization.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which balances quality and memory efficiency.

What speed will mistral small 3.1 24b instruct 2503 hf run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on NVIDIA A16 64GB receives a C grade with 32.0 tok/s and 238K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 238K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GBSee all hardware for mistral small 3.1 24b instruct 2503 hf
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