Can DeepSeek R1 Distill Llama 8B run on RX 9060 XT 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

DeepSeek R1 Distill Llama 8B needs ~8.3 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 8.3 GB, 41.3 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

41.3 tok/s

TTFT

4686 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 Distill Llama 8B on RX 9060 XT 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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 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	41.3 tok/s	2556 ms	147K
Coding	C	Runs well	41.3 tok/s	4686 ms	147K
Agentic Coding	C	Runs well	41.3 tok/s	6817 ms	147K
Reasoning	C	Runs well	41.3 tok/s	5538 ms	147K
RAG	C	Runs well	41.3 tok/s	8521 ms	147K

Inference speed

DeepSeek R1 Distill Llama 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill Llama 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

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on RX 9060 XT 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

Get started

Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Llama 8B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 138%.98.4 tok/s decode

Raises estimated decode speed by about 138%.

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

~$899 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 148%.102.3 tok/s decode

Raises estimated decode speed by about 148%.

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

~$2,000 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run DeepSeek R1 Distill Llama 8B?

Yes, RX 9060 XT 16GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs well). Expected decode speed: 41.3 tok/s.

How much VRAM does DeepSeek R1 Distill Llama 8B need?

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

What is the best quantization for DeepSeek R1 Distill Llama 8B?

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

What speed will DeepSeek R1 Distill Llama 8B run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, DeepSeek R1 Distill Llama 8B achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4686ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on RX 9060 XT 16GB receives a C grade with 41.3 tok/s and 147K context.

What context window can DeepSeek R1 Distill Llama 8B use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, DeepSeek R1 Distill Llama 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 9060 XT 16GB See all hardware for DeepSeek R1 Distill Llama 8B

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