MacBook Pro M3 Max 128GBGünstige Wahl
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.5.6 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Can Llama 3.3 70B Instruct run on Mac Studio M3 Ultra 96GB?
YES — Tight Fit
C48Usable
Estimated from fit model
Llama 3.3 70B Instruct needs ~62.2 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~13 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: 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) — 62.2 GB, 13.0 tok/s, Tight fit
62.2 GB required69.1 GB available
90% VRAM used
Fit status
Tight fit
Decode
13.0 tok/s
TTFT
14844 ms
Safe context
30K
Memory
62.2 GB / 69.1 GB
Memory breakdown
Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom10.4 GB
See how fast it feels
See how fast it feelsLlama 3.3 70B Instruct on Mac Studio M3 Ultra 96GB
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: 13.0 tok/s decode · 14.8s TTFT (warm) · 33 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 13.0 tok/s | 8097 ms | 30K |
| Coding | C | Tight fit | 13.0 tok/s | 14844 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.8 GB host RAM) | 12.5 tok/s | 22488 ms | 30K |
| Reasoning | C | Tight fit | 13.0 tok/s | 17542 ms | 30K |
| RAG | C | Runs with offload (needs ~0.8 GB host RAM) | 12.5 tok/s | 28109 ms | 30K |
Inference speed
Llama 3.3 70B Instruct inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Llama 3.3 70B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 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? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 14.6 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 10.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 9.9 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 7.0 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.2 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 4.7 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 3.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.4 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.3 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
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 3.3 70B Instruct (70B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C45 |
Q3_K_S | 3 | 34.3 GB | Low | C47 |
NVFP4 | 4 | 39.2 GB | Medium | C48 |
Q4_K_M | 4 | 42.7 GB | Medium | C48 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | C48 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3.3 70B Instruct on your machine.
Run
lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server startUpgrade-Optionen
Hardware, die Llama 3.3 70B Instruct gut ausführt
Mac Studio M2 Ultra 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.10.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Mac Studio M1 Ultra 128GBApple-Upgrade
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.10.3 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
NVIDIA H100 80GBGrößter Sprung3350 GB/s (+2531)
B
Raises estimated decode speed by about 407%.65.9 tok/s Dekodierung
Raises estimated decode speed by about 407%.
ca. $40,000 MSRP
Frequently asked questions
Can Mac Studio M3 Ultra 96GB run Llama 3.3 70B Instruct?
Yes, Mac Studio M3 Ultra 96GB can run Llama 3.3 70B Instruct with a C grade (Tight fit). Expected decode speed: 13.0 tok/s.
How much VRAM does Llama 3.3 70B Instruct need?
Llama 3.3 70B Instruct (70B parameters) requires approximately 62.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.3 70B Instruct?
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.3 70B Instruct run at on Mac Studio M3 Ultra 96GB?
On Mac Studio M3 Ultra 96GB, Llama 3.3 70B Instruct achieves approximately 13.0 tokens per second decode speed with a time-to-first-token of 14844ms using Q4_K_M quantization.
Can Mac Studio M3 Ultra 96GB run Llama 3.3 70B Instruct for coding?
For coding workloads, Llama 3.3 70B Instruct on Mac Studio M3 Ultra 96GB receives a C grade with 13.0 tok/s and 30K context.
What context window can Llama 3.3 70B Instruct use on Mac Studio M3 Ultra 96GB?
On Mac Studio M3 Ultra 96GB, Llama 3.3 70B Instruct can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for Llama 3.3 70B Instruct?
Not always. Mac Studio M3 Ultra 96GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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