Mac mini M4 64GBGünstige Wahl
64 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.14 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,099 MSRP
Can Llama 3.2 1B run on Radeon Pro W6800 32GB?
YES — Runs Great
C44Usable
Estimated from fit model
Llama 3.2 1B needs ~5.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~14 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) — 5.2 GB, 14.0 tok/s, Runs well
5.2 GB required32.0 GB available
16% VRAM used
Fit status
Runs well
Decode
14.0 tok/s
TTFT
13829 ms
Safe context
128K
Memory
5.2 GB / 32.0 GB
Memory breakdown
Weights0.6 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 1B on Radeon Pro W6800 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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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 | 14.0 tok/s | 7543 ms | 128K |
| Coding | C | Runs well | 14.0 tok/s | 13829 ms | 128K |
| Agentic Coding | C | Runs well | 14.0 tok/s | 20114 ms | 128K |
| Reasoning | C | Runs well | 14.0 tok/s | 16343 ms | 128K |
| RAG | C | Runs well | 14.0 tok/s | 25143 ms | 128K |
Inference speed
Llama 3.2 1B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Llama 3.2 1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~19 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 | 19.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 16.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 16.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 14.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 14.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 14.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 14.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 14.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 14.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 14.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 14.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 14.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 14.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 14.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 14.0 | Fits |
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 Llama 3.2 1B (1B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C47 |
Q3_K_S | 3 | 0.5 GB | Low | C47 |
NVFP4 | 4 | 0.6 GB | Medium | C47 |
Q4_K_M | 4 | 0.6 GB | Medium | C47 |
Q5_K_M | 5 | 0.7 GB | High | C47 |
Q6_K | 6 | 0.8 GB | High | C47 |
Q8_0 | 8 | 1.1 GB | Very High | C47 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | C47 |
Get started
Copy-paste commands to run Llama 3.2 1B on your machine.
Run
ollama run llama3.2:1bUpgrade-Optionen
Hardware, die Llama 3.2 1B gut ausführt
Frequently asked questions
Can Radeon Pro W6800 32GB run Llama 3.2 1B?
Yes, Radeon Pro W6800 32GB can run Llama 3.2 1B with a C grade (Runs well). Expected decode speed: 14.0 tok/s.
How much VRAM does Llama 3.2 1B need?
Llama 3.2 1B (1B parameters) requires approximately 5.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 1B?
The recommended quantization for Llama 3.2 1B is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 1B run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Llama 3.2 1B achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Llama 3.2 1B for coding?
For coding workloads, Llama 3.2 1B on Radeon Pro W6800 32GB receives a C grade with 14.0 tok/s and 128K context.
What context window can Llama 3.2 1B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Llama 3.2 1B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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