vntl llama3 8b v2 needs ~8.3 GB VRAM. RX 6800 16GB has 16.0 GB. With Q4_K_M quantization, expect ~58 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
Runs well
Decode
57.7 tok/s
TTFT
3355 ms
Safe context
147K
Memory
8.3 GB / 16.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 57.7 tok/s | 1830 ms | 147K |
| Coding | C | Runs well | 57.7 tok/s | 3355 ms | 147K |
| Agentic Coding | C | Runs well | 57.7 tok/s | 4880 ms | 147K |
| Reasoning | C | Runs well | 57.7 tok/s | 3965 ms | 147K |
| RAG | C | Runs well | 57.7 tok/s | 6100 ms | 147K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on RX 6800 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 |
Copy-paste commands to run vntl llama3 8b v2 on your machine.
Run
lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server startYes, RX 6800 16GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 57.7 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.
The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.
On RX 6800 16GB, vntl llama3 8b v2 achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3355ms using Q4_K_M quantization.
For coding workloads, vntl llama3 8b v2 on RX 6800 16GB receives a C grade with 57.7 tok/s and 147K context.
On RX 6800 16GB, vntl llama3 8b v2 can safely use up to 147K 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-lmg-anon--vntl-llama3-8b-v2-gguf-on-rx-6800-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C49 |
Q5_K_M | 5 | 5.8 GB | High | C50 |
Q6_K | 6 | 6.6 GB | High | C51 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C52 |
F16 | 16 | 16.4 GB | Maximum | F0 |