Llama 3 8B Instruct 32k v0.1 needs ~7.5 GB VRAM. RX 6600 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
22.5 tok/s
TTFT
8608 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 | 22.5 tok/s | 4695 ms | 24K |
| Coding | C | Tight fit | 22.5 tok/s | 8608 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 18755 ms | 24K |
| Reasoning | C | Tight fit | 22.5 tok/s | 10173 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 23444 ms | 24K |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RX 6600 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 |
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-Optionen
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
ca. $329 MSRP
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
ca. $449 MSRP
Yes, RX 6600 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Tight fit). Expected decode speed: 22.5 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 6600 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8608ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RX 6600 8GB receives a C grade with 22.5 tok/s and 24K context.
On RX 6600 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-6600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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