RX 7600 XT 16GBGünstige Wahl
16 GB VRAM (+8)
C
Raises estimated decode speed by about 39%.30.4 tok/s Dekodierung
Raises estimated decode speed by about 39%.
Adds memory headroom for longer context windows and future model growth.
ca. $329 MSRP
Can HelpingAI2 9B i1 run on Radeon PRO W7600 8GB?
YES — With Offload
C49Usable
Estimated from fit model
HelpingAI2 9B i1 needs ~8.2 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.2 GB, 21.8 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
21.8 tok/s
TTFT
8886 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsHelpingAI2 9B i1 on Radeon PRO W7600 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 31.0 tok/s | 3412 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 21.8 tok/s | 8886 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 16.9 tok/s | 16653 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 21.8 tok/s | 10502 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 16.9 tok/s | 20816 ms | 12K |
Inference speed
HelpingAI2 9B i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for HelpingAI2 9B 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.
Quantization options
How HelpingAI2 9B i1 (9B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI2 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server startUpgrade-Optionen
Hardware, die HelpingAI2 9B i1 gut ausführt
RX 9060 XT 16GBBestes Preis-Leistungs-Verhältnis
16 GB VRAM (+8)320 GB/s (+32)
C
Raises estimated decode speed by about 68%.36.7 tok/s Dekodierung
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
RX 7700 XT 12GBAMD-Upgrade
12 GB VRAM (+4)432 GB/s (+144)
C
Raises estimated decode speed by about 117%.47.2 tok/s Dekodierung
Raises estimated decode speed by about 117%.
Adds memory headroom for longer context windows and future model growth.
ca. $449 MSRP
Frequently asked questions
Can Radeon PRO W7600 8GB run HelpingAI2 9B i1?
Yes, Radeon PRO W7600 8GB can run HelpingAI2 9B i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 21.8 tok/s.
How much VRAM does HelpingAI2 9B i1 need?
HelpingAI2 9B i1 (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2 9B i1?
The recommended quantization for HelpingAI2 9B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2 9B i1 run at on Radeon PRO W7600 8GB?
On Radeon PRO W7600 8GB, HelpingAI2 9B i1 achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8886ms using Q4_K_M quantization.
Can Radeon PRO W7600 8GB run HelpingAI2 9B i1 for coding?
For coding workloads, HelpingAI2 9B i1 on Radeon PRO W7600 8GB receives a C grade with 21.8 tok/s and 12K context.
What context window can HelpingAI2 9B i1 use on Radeon PRO W7600 8GB?
On Radeon PRO W7600 8GB, HelpingAI2 9B i1 can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if HelpingAI2 9B i1 feels slow on Radeon PRO W7600 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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