Can Llama 3.3 70B run on RX 9060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Llama 3.3 70B needs ~49.3 GB but RX 9060 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) — 49.3 GB, exceeds 8.0 GB available

49.3 GB required8.0 GB available

616% VRAM needed

41.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

49.3 GB / 8.0 GB

Offload

80%

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.3 70B on RX 9060 8GB
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: 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 49.3 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	F0
Q3_K_S	3	34.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Llama 3.3 70B well

Radeon Pro W7900 48GBBest value

48 GB VRAM (+40)864 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.7.8 tok/s decode

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

Raises estimated decode speed by about 290%.

~$3,999 MSRP

Radeon PRO W7900 DS 48GBAMD upgrade

48 GB VRAM (+40)864 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.7.8 tok/s decode

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

Raises estimated decode speed by about 290%.

~$3,999 MSRP

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+280)8000 GB/s (+7712)

Makes the model fit on the accelerator instead of staying completely out of reach.148.7 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.

~$8,000 MSRP

NVIDIA H100 80GBBiggest leap

80 GB VRAM (+72)3350 GB/s (+3062)

Makes the model fit on the accelerator instead of staying completely out of reach.71.7 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.

~$40,000 MSRP

Frequently asked questions

Can RX 9060 8GB run Llama 3.3 70B?

No, Llama 3.3 70B requires more memory than RX 9060 8GB provides.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 49.3 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 RX 9060 8GB?

On RX 9060 8GB, Llama 3.3 70B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RX 9060 8GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on RX 9060 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Llama 3.3 70B use on RX 9060 8GB?

On RX 9060 8GB, Llama 3.3 70B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.3 70B feels slow on RX 9060 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.

See all results for RX 9060 8GB See all hardware for Llama 3.3 70B

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