RX 9060 XT 16GBBudget pick
16 GB VRAM (+8)320 GB/s (+96)
C
Raises estimated decode speed by about 53%.42.4 tok/s decode
Raises estimated decode speed by about 53%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
exaone 3.0 7.8b it needs ~7.4 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 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
27.8 tok/s
TTFT
6970 ms
Safe context
27K
Memory
7.4 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 | 27.8 tok/s | 3802 ms | 27K |
| Coding | C | Tight fit | 27.8 tok/s | 6970 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 19.3 tok/s | 14555 ms | 27K |
| Reasoning | C | Tight fit | 27.8 tok/s | 8237 ms | 27K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 19.3 tok/s | 18194 ms |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C54 |
Q3_K_S | 3 | 3.8 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startUpgrade options
Raises estimated decode speed by about 53%.
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 exaone 3.0 7.8b it with a C grade (Tight fit). Expected decode speed: 27.8 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W7500 8GB, exaone 3.0 7.8b it achieves approximately 27.8 tokens per second decode speed with a time-to-first-token of 6970ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on Radeon Pro W7500 8GB receives a C grade with 27.8 tok/s and 27K context.
On Radeon Pro W7500 8GB, exaone 3.0 7.8b it can safely use up to 27K 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-bingsu--exaone-3-0-7-8b-it-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>
Preview:
| 27K |
| Medium |
| C53 |
Q4_K_MBest for your GPU | 4 | 4.8 GB | Medium | C53 |
Q5_K_M | 5 | 5.6 GB | High | F0 |
Q6_K | 6 | 6.4 GB | High | F0 |
Q8_0 | 8 | 8.3 GB | Very High | F0 |
F16 | 16 | 16.0 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.