RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 51%.34.2 tok/s decode
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Meta Llama 3 8B Instruct needs ~7.5 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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
22.6 tok/s
TTFT
8583 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 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 |
How Meta Llama 3 8B Instruct (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 |
Copy-paste commands to run Meta Llama 3 8B Instruct on your machine.
Run
lms load hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf && lms server startUpgrade options
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Yes, RX 590 8GB can run Meta Llama 3 8B Instruct with a C grade (Tight fit). Expected decode speed: 22.6 tok/s.
Meta Llama 3 8B Instruct (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RX 590 8GB, Meta Llama 3 8B Instruct achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8583ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3 8B Instruct on RX 590 8GB receives a C grade with 22.6 tok/s and 24K context.
On RX 590 8GB, Meta Llama 3 8B Instruct 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--meta-llama-3-8b-instruct-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:
| 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.