Will It Run AI

Can Mixtral 8x7B run on AMD Instinct MI60 32GB?

BARELY — Tight on Memory

C55Usable
Estimated from fit model

Mixtral 8x7B needs ~34.7 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 34.7 GB, 22.8 tok/s, Very compromised (needs ~2.2 GB host RAM)
34.7 GB required32.0 GB available
108% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.2 GB host RAM)

Decode

22.8 tok/s

TTFT

8494 ms

Safe context

4K

Memory

34.7 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights28.7 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsMixtral 8x7B on AMD Instinct MI60 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 22.8 tok/s decode · 8.5s TTFT (warm) · 57 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~1.5 GB host RAM)24.2 tok/s4363 ms4K
CodingCVery compromised21.2 tok/s9131 ms4K
Agentic CodingCVery compromised (needs ~3.7 GB host RAM)20.3 tok/s13863 ms4K
ReasoningCVery compromised (needs ~2.2 GB host RAM)22.8 tok/s10038 ms4K
RAGCVery compromised (needs ~3.7 GB host RAM)20.3 tok/s17329 ms4K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.3 GB
LowB65
Q3_K_SBest for your GPU
3
23.0 GB
LowB64
NVFP4
4
26.3 GB
MediumF0
Q4_K_M
4
28.7 GB
MediumF0
Q5_K_M
5
33.8 GB
HighF0
Q6_K
6
38.5 GB
HighF0
Q8_0
8
50.3 GB
Very HighF0
F16
16
96.4 GB
MaximumF0

Get started

Copy-paste commands to run Mixtral 8x7B on your machine.

Run

ollama run mixtral

Opciones de mejora

Hardware que ejecuta bien Mixtral 8x7B

Radeon Pro W7900 48GBOpción económica
48 GB VRAM (+16)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.36.7 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 61%.

~$3,999 MSRP

Radeon PRO W7900 DS 48GBMejor relación calidad-precio
48 GB VRAM (+16)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.36.7 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 61%.

~$3,999 MSRP

AMD Instinct MI350X 288GBMejora AMD
288 GB VRAM (+256)8000 GB/s (+6976)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.420.1 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 1743%.

~$8,000 MSRP

Frequently asked questions

Can AMD Instinct MI60 32GB run Mixtral 8x7B?

Yes, AMD Instinct MI60 32GB can run Mixtral 8x7B with a C grade (Very compromised). Expected decode speed: 21.2 tok/s.

How much VRAM does Mixtral 8x7B need?

Mixtral 8x7B (47B parameters) requires approximately 34.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mixtral 8x7B?

The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mixtral 8x7B run at on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, Mixtral 8x7B achieves approximately 21.2 tokens per second decode speed with a time-to-first-token of 9131ms using Q4_K_M quantization.

Can AMD Instinct MI60 32GB run Mixtral 8x7B for coding?

For coding workloads, Mixtral 8x7B on AMD Instinct MI60 32GB receives a C grade with 21.2 tok/s and 4K context.

What context window can Mixtral 8x7B use on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, Mixtral 8x7B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mixtral 8x7B feels slow on AMD Instinct MI60 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for AMD Instinct MI60 32GBSee all hardware for Mixtral 8x7B
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