RX 7700 XT 12GBBudget pick
12 GB VRAM (+4)432 GB/s (+144)
C
Raises estimated decode speed by about 43%.53.1 tok/s decode
Raises estimated decode speed by about 43%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Llama 3 8B Instruct 32k v0.1 needs ~7.5 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Operating mode
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
Fit status
Tight fit
Decode
37.2 tok/s
TTFT
5207 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 37.2 tok/s | 2840 ms | 24K |
| Coding | C | Tight fit | 37.2 tok/s | 5207 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 24.8 tok/s | 11346 ms | 24K |
| Reasoning | C | Tight fit | 37.2 tok/s | 6154 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 24.8 tok/s | 14182 ms |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RX 9060 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 |
Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.
Run
lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 43%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, RX 9060 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Tight fit). Expected decode speed: 37.2 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.
On RX 9060 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5207ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RX 9060 8GB receives a C grade with 37.2 tok/s and 24K context.
On RX 9060 8GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-rx-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 24K |
| 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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.