HelpingAI2.5 10B i1 needs ~9.0 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
17.5 tok/s
TTFT
11047 ms
Safe context
4K
Memory
9.0 GB / 8.0 GB
Offload
10%
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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 20.2 tok/s | 5227 ms | 4K |
| Coding | D | Very compromised (needs ~0.7 GB host RAM) | 17.5 tok/s | 11047 ms | 4K |
| Agentic Coding | F | Too heavy | 13.5 tok/s | 20807 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.7 GB host RAM) | 17.5 tok/s | 13056 ms | 4K |
| RAG | F | Too heavy | 13.5 tok/s | 26009 ms |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 4.9 GB | Low | C52 |
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 57%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 89%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 143%.
~$449 MSRP
Yes, RX 9060 8GB can run HelpingAI2.5 10B i1 with a D grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 17.5 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
On RX 9060 8GB, HelpingAI2.5 10B i1 achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11047ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RX 9060 8GB receives a D grade with 17.5 tok/s and 4K context.
On RX 9060 8GB, HelpingAI2.5 10B i1 can safely use up to 4K 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-mradermacher--helpingai2-5-10b-i1-gguf-on-rx-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
5.6 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 6.1 GB | Medium | F0 |
Q5_K_M | 5 | 7.2 GB | High | F0 |
Q6_K | 6 | 8.2 GB | High | F0 |
Q8_0 | 8 | 10.7 GB | Very High | F0 |
F16 | 16 | 20.5 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.