MacBook Pro M4 Pro 24GBコスパ重視
24 GB Unified (+8)
C
This setup is broadly balanced for this model.52.8 tok/sデコード
〜$1,999 MSRP
Can HelpingAI2 6B i1 run on RX 9060 XT 16GB?
YES — Runs Great
C49Usable
Estimated from fit model
HelpingAI2 6B i1 needs ~6.9 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 6.9 GB, 55.1 tok/s, Runs well
6.9 GB required16.0 GB available
43% VRAM used
Fit status
Runs well
Decode
55.1 tok/s
TTFT
3515 ms
Safe context
224K
Memory
6.9 GB / 16.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI2 6B i1 on RX 9060 XT 16GB
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: 55.1 tok/s decode · 3.5s TTFT (warm) · 138 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 55.1 tok/s | 1917 ms | 224K |
| Coding | C | Runs well | 55.1 tok/s | 3515 ms | 224K |
| Agentic Coding | C | Runs well | 55.1 tok/s | 5112 ms | 224K |
| Reasoning | C | Runs well | 55.1 tok/s | 4154 ms | 224K |
| RAG | C | Runs well | 55.1 tok/s | 6390 ms | 224K |
Quantization options
How HelpingAI2 6B i1 (6B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C46 |
NVFP4 | 4 | 3.4 GB | Medium | C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C48 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C50 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startアップグレードオプション
HelpingAI2 6B i1を快適に動かすハードウェア
Frequently asked questions
Can RX 9060 XT 16GB run HelpingAI2 6B i1?
Yes, RX 9060 XT 16GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 55.1 tok/s.
How much VRAM does HelpingAI2 6B i1 need?
HelpingAI2 6B i1 (6B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2 6B i1?
The recommended quantization for HelpingAI2 6B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2 6B i1 run at on RX 9060 XT 16GB?
On RX 9060 XT 16GB, HelpingAI2 6B i1 achieves approximately 55.1 tokens per second decode speed with a time-to-first-token of 3515ms using Q4_K_M quantization.
Can RX 9060 XT 16GB run HelpingAI2 6B i1 for coding?
For coding workloads, HelpingAI2 6B i1 on RX 9060 XT 16GB receives a C grade with 55.1 tok/s and 224K context.
What context window can HelpingAI2 6B i1 use on RX 9060 XT 16GB?
On RX 9060 XT 16GB, HelpingAI2 6B i1 can safely use up to 224K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-6b-i1-gguf-on-rx-9060-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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