RX 7600 XT 16GBコスパ重視
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 51%.34.2 tok/sデコード
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can DeepSeek R1 Distill Llama 8B run on RX 590 8GB?
YES — Tight Fit
C50Usable
Estimated from fit model
DeepSeek R1 Distill Llama 8B needs ~7.5 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.5 GB, 22.6 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
22.6 tok/s
TTFT
8583 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill Llama 8B on RX 590 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: 22.6 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 22.6 tok/s | 4681 ms | 24K |
| Coding | C | Tight fit | 22.6 tok/s | 8583 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 19325 ms | 24K |
| Reasoning | C | Tight fit | 22.6 tok/s | 10143 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 24157 ms | 24K |
Quantization options
How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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アップグレードオプション
DeepSeek R1 Distill Llama 8Bを快適に動かすハードウェア
RX 9060 XT 16GBコスパ最良
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 83%.41.3 tok/sデコード
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
RX 7700 XT 12GBAMDアップグレード
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 135%.53.1 tok/sデコード
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Frequently asked questions
Can RX 590 8GB run DeepSeek R1 Distill Llama 8B?
Yes, RX 590 8GB can run DeepSeek R1 Distill Llama 8B with a C grade (Tight fit). Expected decode speed: 22.6 tok/s.
How much VRAM does DeepSeek R1 Distill Llama 8B need?
DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 7.5 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 590 8GB?
On RX 590 8GB, DeepSeek R1 Distill Llama 8B achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8583ms using Q4_K_M quantization.
Can RX 590 8GB run DeepSeek R1 Distill Llama 8B for coding?
For coding workloads, DeepSeek R1 Distill Llama 8B on RX 590 8GB receives a C grade with 22.6 tok/s and 24K context.
What context window can DeepSeek R1 Distill Llama 8B use on RX 590 8GB?
On RX 590 8GB, DeepSeek R1 Distill Llama 8B can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if DeepSeek R1 Distill Llama 8B feels slow on RX 590 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--deepseek-r1-distill-llama-8b-gguf-on-rx-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: