MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
C
This setup is broadly balanced for this model.76.8 tok/s Dekodierung
ca. $2,499 MSRP
Can vntl llama3 8b v2 run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
vntl llama3 8b v2 needs ~9.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.9 GB, 77.4 tok/s, Runs well
9.9 GB required32.0 GB available
31% VRAM used
Fit status
Runs well
Decode
77.4 tok/s
TTFT
2502 ms
Safe context
393K
Memory
9.9 GB / 32.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsvntl llama3 8b v2 on Radeon AI PRO R9700 32GB
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: 77.4 tok/s decode · 2.5s TTFT (warm) · 193 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 | 77.4 tok/s | 1365 ms | 393K |
| Coding | C | Runs well | 77.4 tok/s | 2502 ms | 393K |
| Agentic Coding | C | Runs well | 77.4 tok/s | 3639 ms | 393K |
| Reasoning | C | Runs well | 77.4 tok/s | 2957 ms | 393K |
| RAG | C | Runs well | 77.4 tok/s | 4549 ms | 393K |
Quantization options
How vntl llama3 8b v2 (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 | 4.5 GB | Medium | C44 |
Q4_K_M | 4 | 4.9 GB | Medium | C44 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
Get started
Copy-paste commands to run vntl llama3 8b v2 on your machine.
Run
lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server startUpgrade-Optionen
Hardware, die vntl llama3 8b v2 gut ausführt
Frequently asked questions
Can Radeon AI PRO R9700 32GB run vntl llama3 8b v2?
Yes, Radeon AI PRO R9700 32GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 77.4 tok/s.
How much VRAM does vntl llama3 8b v2 need?
vntl llama3 8b v2 (8B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
What is the best quantization for vntl llama3 8b v2?
The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.
What speed will vntl llama3 8b v2 run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, vntl llama3 8b v2 achieves approximately 77.4 tokens per second decode speed with a time-to-first-token of 2502ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run vntl llama3 8b v2 for coding?
For coding workloads, vntl llama3 8b v2 on Radeon AI PRO R9700 32GB receives a C grade with 77.4 tok/s and 393K context.
What context window can vntl llama3 8b v2 use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, vntl llama3 8b v2 can safely use up to 393K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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