vntl llama3 8b v2 needs ~7.8 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~39 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 with offload
Decode
38.7 tok/s
TTFT
5004 ms
Safe context
19K
Memory
7.8 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 | 38.7 tok/s | 2729 ms | 19K |
| Coding | C | Runs with offload | 38.7 tok/s | 5004 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 12164 ms | 19K |
| Reasoning | C | Runs with offload | 38.7 tok/s | 5914 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 15205 ms | 19K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on GTX 1080 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 | C53 |
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 vntl llama3 8b v2 on your machine.
Run
lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server startOpciones de mejora
RTX 3060 12GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 26%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
RTX 5060 Ti 16GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 47%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
RTX 5070 12GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 124%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$549 MSRP
Yes, GTX 1080 8GB can run vntl llama3 8b v2 with a C grade (Runs with offload). Expected decode speed: 38.7 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 7.8 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 GTX 1080 8GB, vntl llama3 8b v2 achieves approximately 38.7 tokens per second decode speed with a time-to-first-token of 5004ms using Q4_K_M quantization.
For coding workloads, vntl llama3 8b v2 on GTX 1080 8GB receives a C grade with 38.7 tok/s and 19K context.
On GTX 1080 8GB, vntl llama3 8b v2 can safely use up to 19K 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-lmg-anon--vntl-llama3-8b-v2-gguf-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: