Can mistral small 3.1 24b instruct 2503 hf run on RX 590 8GB?

Q: What should I upgrade first if mistral small 3.1 24b instruct 2503 hf feels slow on RX 590 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~19.2 GB but RX 590 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 19.2 GB, exceeds 8.0 GB available

19.2 GB required8.0 GB available

240% VRAM needed

11.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

19.2 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on RX 590 8GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 19.2 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

mistral small 3.1 24b instruct 2503 hf inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for mistral small 3.1 24b instruct 2503 hf at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	82.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	52.3	Tight

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4

Upgrade options

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

RX 7900 XT 20GBBudget pick

20 GB VRAM (+12)800 GB/s (+544)

Makes the model fit on the accelerator instead of staying completely out of reach.23.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+16)960 GB/s (+704)

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+24)640 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.25.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 590 8GB run mistral small 3.1 24b instruct 2503 hf?

No, mistral small 3.1 24b instruct 2503 hf requires more memory than RX 590 8GB provides.

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 19.2 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 RX 590 8GB?

On RX 590 8GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RX 590 8GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on RX 590 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on RX 590 8GB?

On RX 590 8GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 590 8GB See all hardware for mistral small 3.1 24b instruct 2503 hf

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