Can DeepSeek R1 0528 Qwen3 8B run on RX 9070 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~8.3 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~81 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.3 GB, 81.3 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

81.3 tok/s

TTFT

2381 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on RX 9070 16GB
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: 81.3 tok/s decode · 2.4s TTFT (warm) · 203 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	C	Runs well	81.3 tok/s	1299 ms	147K
Coding	C	Runs well	81.3 tok/s	2381 ms	147K
Agentic Coding	C	Runs well	81.3 tok/s	3463 ms	147K
Reasoning	C	Runs well	81.3 tok/s	2814 ms	147K
RAG	C	Runs well	81.3 tok/s	4329 ms	147K

Inference speed

DeepSeek R1 0528 Qwen3 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 0528 Qwen3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	Fits

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 DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.

Run

lms load hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf && lms server start

Frequently asked questions

Can RX 9070 16GB run DeepSeek R1 0528 Qwen3 8B?

Yes, RX 9070 16GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 81.3 tok/s.

How much VRAM does DeepSeek R1 0528 Qwen3 8B need?

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 0528 Qwen3 8B?

The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 0528 Qwen3 8B run at on RX 9070 16GB?

On RX 9070 16GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 81.3 tokens per second decode speed with a time-to-first-token of 2381ms using Q4_K_M quantization.

Can RX 9070 16GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on RX 9070 16GB receives a C grade with 81.3 tok/s and 147K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on RX 9070 16GB?

On RX 9070 16GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9070 16GB See all hardware for DeepSeek R1 0528 Qwen3 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf-on-rx-9070-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

81.3 tok/s

1299 ms

147K

Coding

Runs well

81.3 tok/s

2381 ms

147K

Agentic Coding

Runs well

81.3 tok/s

3463 ms

147K

Reasoning

Runs well

81.3 tok/s

2814 ms

147K

RAG

Runs well

81.3 tok/s

4329 ms

147K

DeepSeek R1 0528 Qwen3 8B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

112.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

112.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

112.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

112.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

112.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

112.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

95.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

90.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

77.5

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

76.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

76.8

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

49.2

Fits

RTX 3060 12GB

12 GB

Q4_K_M

48.7

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

45.1

Fits

RTX 4060 8GB

8 GB

Q4_K_M

40.7

Offloads

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

39.6

Fits

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

Frequently asked questions

Can RX 9070 16GB run DeepSeek R1 0528 Qwen3 8B?

Yes, RX 9070 16GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 81.3 tok/s.

How much VRAM does DeepSeek R1 0528 Qwen3 8B need?

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 0528 Qwen3 8B?

The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 0528 Qwen3 8B run at on RX 9070 16GB?

On RX 9070 16GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 81.3 tokens per second decode speed with a time-to-first-token of 2381ms using Q4_K_M quantization.

Can RX 9070 16GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on RX 9070 16GB receives a C grade with 81.3 tok/s and 147K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on RX 9070 16GB?

On RX 9070 16GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.