HelpingAI 15B i1 needs ~9.3 GB VRAM. RX 7600 8GB has 8.0 GB. With Q2_K quantization, expect ~13 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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36837 ms
Safe context
4K
Memory
12.6 GB / 8.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.1 tok/s | 17258 ms | 4K |
| Coding | F | Too heavy | 5.3 tok/s | 36837 ms | 4K |
| Agentic Coding | F | Too heavy | 4.0 tok/s | 70524 ms | 4K |
| Reasoning | F | Too heavy | 5.3 tok/s | 43535 ms | 4K |
| RAG | F | Too heavy | 4.0 tok/s | 88155 ms | 4K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | F0 |
Q3_K_S | 3 | 7.4 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startUpgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 238%.
~$449 MSRP
RTX A4500 20GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,000 MSRP
Yes, RX 7600 8GB can run HelpingAI 15B i1 at Q2_K quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 12.6 GB which exceeds available memory, but at Q2_K it needs only 9.3 GB. Expected decode speed: 13.2 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 12.6 GB at Q4_K_M quantization. On RX 7600 8GB, it fits at Q2_K using 9.3 GB.
The recommended quantization is Q4_K_M, but on RX 7600 8GB the best fitting quantization is Q2_K, which uses 9.3 GB.
On RX 7600 8GB, HelpingAI 15B i1 achieves approximately 13.2 tokens per second decode speed with a time-to-first-token of 14628ms using Q2_K quantization.
For coding workloads, HelpingAI 15B i1 on RX 7600 8GB receives a F grade with 5.3 tok/s and 4K context.
On RX 7600 8GB, HelpingAI 15B i1 can safely use up to 4K tokens of context at Q2_K quantization. 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-mradermacher--helpingai-15b-i1-gguf-on-rx-7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 9.2 GB | Medium | F0 |
Q5_K_M | 5 | 10.8 GB | High | F0 |
Q6_K | 6 | 12.3 GB | High | F0 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.