RX 9060 XT 16GBBudget pick
16 GB VRAM (+8)320 GB/s (+96)
C
Raises estimated decode speed by about 52%.41.3 tok/s decode
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
vntl llama3 8b v2 needs ~7.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 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
27.1 tok/s
TTFT
7149 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 | 27.1 tok/s | 3899 ms | 24K |
| Coding | C | Tight fit | 27.1 tok/s | 7149 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 18.1 tok/s | 15576 ms | 24K |
| Reasoning | C | Tight fit | 27.1 tok/s | 8448 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 18.1 tok/s | 19470 ms | 24K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on Radeon Pro W7500 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 startUpgrade options
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 96%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, Radeon Pro W7500 8GB can run vntl llama3 8b v2 with a C grade (Tight fit). Expected decode speed: 27.1 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 7.5 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 Radeon Pro W7500 8GB, vntl llama3 8b v2 achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7149ms using Q4_K_M quantization.
For coding workloads, vntl llama3 8b v2 on Radeon Pro W7500 8GB receives a C grade with 27.1 tok/s and 24K context.
On Radeon Pro W7500 8GB, vntl llama3 8b v2 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-lmg-anon--vntl-llama3-8b-v2-gguf-on-radeon-pro-w7500-8gb" 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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