Can mistral small 3.1 24b instruct 2503 hf run on RTX A6000 48GB?

YES — Runs Great

C50Usable

○Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~23.5 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.5 GB, 39.9 tok/s, Runs well

23.5 GB required48.0 GB available

49% VRAM used

Fit status

Runs well

Decode

39.9 tok/s

TTFT

4856 ms

Safe context

156K

Memory

23.5 GB / 48.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on RTX A6000 48GB
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: 39.9 tok/s decode · 4.9s TTFT (warm) · 100 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	39.9 tok/s	2649 ms	156K
Coding	C	Runs well	39.9 tok/s	4856 ms	156K
Agentic Coding	C	Runs well	39.9 tok/s	7063 ms	156K
Reasoning	C	Runs well	39.9 tok/s	5739 ms	156K
RAG	C	Runs well	39.9 tok/s	8829 ms	156K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C43
Q3_K_S	3	11.8 GB	Low	C43
NVFP4	4

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

Upgrade options

Hardware that runs mistral small 3.1 24b instruct 2503 hf well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+870)

Raises estimated decode speed by about 91%.76.1 tok/s decode

Raises estimated decode speed by about 91%.

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

~$10,000 MSRP

Frequently asked questions

See all results for RTX A6000 48GB See all hardware for mistral small 3.1 24b instruct 2503 hf

URL: https://willitrunai.com/can-run/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-a6000-48gb

⇱