HelpingAI 9B 200k i1 needs ~36.2 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~126 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 well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
3.8M
Memory
36.2 GB / 288.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 126.0 tok/s | 838 ms | 3.8M |
| Coding | C | Runs well | 126.0 tok/s | 1537 ms | 3.8M |
| Agentic Coding | C | Runs well | 126.0 tok/s | 2235 ms | 3.8M |
| Reasoning | C | Runs well | 126.0 tok/s | 1816 ms | 3.8M |
| RAG | C | Runs well | 126.0 tok/s | 2794 ms | 3.8M |
Inference speed
Estimated decode speed (tokens/sec) for HelpingAI 9B 200k i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 125.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 119.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 111.3 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 101.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 80.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 68.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 43.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 43.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 40.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 35.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.4 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D35 |
Q3_K_S | 3 | 4.4 GB | Low | D35 |
NVFP4 | 4 | 5.0 GB | Medium | D35 |
Q4_K_M | 4 | 5.5 GB | Medium | D35 |
Q5_K_M | 5 | 6.5 GB | High | D35 |
Q6_K | 6 | 7.4 GB | High | D35 |
Q8_0 | 8 | 9.6 GB | Very High | D35 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | D36 |
Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server startYes, AMD Instinct MI350X 288GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 126.0 tok/s.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 36.2 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI350X 288GB, HelpingAI 9B 200k i1 achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on AMD Instinct MI350X 288GB receives a C grade with 126.0 tok/s and 3.8M context.
On AMD Instinct MI350X 288GB, HelpingAI 9B 200k i1 can safely use up to 3.8M 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--helpingai-9b-200k-i1-gguf-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: