Can Mistral Nemo 12B run on RX 6700 XT 12GB?

Q: What should I upgrade first if Mistral Nemo 12B feels slow on RX 6700 XT 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

B63Good

Estimated from fit model

Mistral Nemo 12B needs ~11.9 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.9 GB, 29.3 tok/s, Runs with offload

11.9 GB required12.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

29.3 tok/s

TTFT

6602 ms

Safe context

17K

Memory

11.9 GB / 12.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMistral Nemo 12B on RX 6700 XT 12GB
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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 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	B	Tight fit	29.3 tok/s	3601 ms	17K
Coding	B	Runs with offload	29.3 tok/s	6602 ms	17K
Agentic Coding	C	Very compromised (needs ~1.2 GB host RAM)	15.2 tok/s	18528 ms	17K
Reasoning	B	Runs with offload	29.3 tok/s	7802 ms	17K
RAG	C	Very compromised (needs ~1.2 GB host RAM)	15.2 tok/s	23160 ms

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B64
Q3_K_S	3	5.9 GB	Low	B65
NVFP4	4

Get started

Copy-paste commands to run Mistral Nemo 12B on your machine.

Run

ollama run mistral-nemo

Upgrade options

Hardware that runs Mistral Nemo 12B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.24.5 tok/s decode

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.29.6 tok/s decode

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

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+256)

Raises estimated decode speed by about 99%.58.3 tok/s decode

Raises estimated decode speed by about 99%.

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

~$479 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run Mistral Nemo 12B?

Yes, RX 6700 XT 12GB can run Mistral Nemo 12B with a B grade (Runs with offload). Expected decode speed: 29.3 tok/s.

How much VRAM does Mistral Nemo 12B need?

Mistral Nemo 12B (12B parameters) requires approximately 11.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Nemo 12B?

The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Nemo 12B run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Mistral Nemo 12B achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6602ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on RX 6700 XT 12GB receives a B grade with 29.3 tok/s and 17K context.

What context window can Mistral Nemo 12B use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Mistral Nemo 12B can safely use up to 17K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Nemo 12B feels slow on RX 6700 XT 12GB?

See all results for RX 6700 XT 12GB See all hardware for Mistral Nemo 12B

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