MacBook Pro M3 Pro 18GBコスパ重視
18 GB Unified (+6)
C
This setup is broadly balanced for this model.42 tok/sデコード
〜$1,999 MSRP
Can HELVETE 3B run on RX 6750 XT 12GB?
YES — Runs Great
C47Usable
Estimated from fit model
HELVETE 3B needs ~4.3 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 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) — 4.3 GB, 42.0 tok/s, Runs well
4.3 GB required12.0 GB available
36% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
367K
Memory
4.3 GB / 12.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsHELVETE 3B on RX 6750 XT 12GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | 42.0 tok/s | 2514 ms | 367K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 367K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 367K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 367K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 367K |
Quantization options
How HELVETE 3B (3B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C47 |
Q3_K_S | 3 | 1.5 GB | Low | C47 |
NVFP4 | 4 | 1.7 GB | Medium | C48 |
Q4_K_M | 4 | 1.8 GB | Medium | C48 |
Q5_K_M | 5 | 2.2 GB | High | C48 |
Q6_K | 6 | 2.5 GB | High | C49 |
Q8_0 | 8 | 3.2 GB | Very High | C49 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C52 |
Get started
Copy-paste commands to run HELVETE 3B on your machine.
Run
lms load hf-helpingai--helvete-3b && lms server startアップグレードオプション
HELVETE 3Bを快適に動かすハードウェア
Frequently asked questions
Can RX 6750 XT 12GB run HELVETE 3B?
Yes, RX 6750 XT 12GB can run HELVETE 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does HELVETE 3B need?
HELVETE 3B (3B parameters) requires approximately 4.3 GB of memory with Q4_K_M quantization.
What is the best quantization for HELVETE 3B?
The recommended quantization for HELVETE 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will HELVETE 3B run at on RX 6750 XT 12GB?
On RX 6750 XT 12GB, HELVETE 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can RX 6750 XT 12GB run HELVETE 3B for coding?
For coding workloads, HELVETE 3B on RX 6750 XT 12GB receives a C grade with 42.0 tok/s and 367K context.
What context window can HELVETE 3B use on RX 6750 XT 12GB?
On RX 6750 XT 12GB, HELVETE 3B can safely use up to 367K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-helpingai--helvete-3b-on-rx-6750-xt-12gb" 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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