Can Llama 3.3 70B run on AMD Instinct MI350X 288GB?

YES — Runs Great

A82Great

Estimated from fit model

Llama 3.3 70B needs ~77.3 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~149 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 77.3 GB, 148.7 tok/s, Runs well

77.3 GB required288.0 GB available

27% VRAM used

Fit status

Runs well

Decode

148.7 tok/s

TTFT

1302 ms

Safe context

128K

Memory

77.3 GB / 288.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B on AMD Instinct MI350X 288GB
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: 148.7 tok/s decode · 1.3s TTFT (warm) · 372 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	A	Runs well	148.7 tok/s	710 ms	128K
Coding	A	Runs well	148.7 tok/s	1302 ms	128K
Agentic Coding	A	Runs well	148.7 tok/s	1893 ms	128K
Reasoning	A	Runs well	148.7 tok/s	1538 ms	128K
RAG	A	Runs well	148.7 tok/s	2367 ms	128K

Inference speed

Llama 3.3 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.3 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~18 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	18.0	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	15.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	14.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.6	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.2	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	9.5	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	8.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	8.7	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.7	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.4	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.3	Too big
RTX 4090 24GB	24 GB	Q4_K_M	3.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.7	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.5	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A72
Q3_K_S	3	34.3 GB	Low	A72
NVFP4	4	39.2 GB	Medium	A73
Q4_K_M	4	42.7 GB	Medium	A73
Q5_K_M	5	50.4 GB	High	A73
Q6_K	6	57.4 GB	High	A74
Q8_0	8	74.9 GB	Very High	A75
F16Best for your GPU	16	143.5 GB	Maximum	A80

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your AMD Instinct MI350X 288GB can run

Model	Params	Grade	Decode
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
Devstral 2 123B Instruct	123B	S	84.6 tok/s
Qwen 3.5 122B A10B	122B	S	234.8 tok/s
DeepSeek V4 Flash	284B	S	125.8 tok/s
Mistral Small 4 119B	119B	S	254.6 tok/s

Frequently asked questions

Can AMD Instinct MI350X 288GB run Llama 3.3 70B?

Yes, AMD Instinct MI350X 288GB can run Llama 3.3 70B with a A grade (Runs well). Expected decode speed: 148.7 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 77.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Llama 3.3 70B achieves approximately 148.7 tokens per second decode speed with a time-to-first-token of 1302ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on AMD Instinct MI350X 288GB receives a A grade with 148.7 tok/s and 128K context.

What context window can Llama 3.3 70B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for Llama 3.3 70B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llama-3.3-70b-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: