RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視1792 GB/s (+992)
A
Raises estimated decode speed by about 224%.58 tok/sデコード
Raises estimated decode speed by about 224%.
〜$9,999 MSRP
Can Falcon 40B Instruct run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
B68Good
Estimated from fit model
Falcon 40B Instruct needs ~45.7 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q5_K_M quantization, expect ~18 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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
Q5_K_M (High quality) — 45.7 GB, 17.9 tok/s, Runs well
45.7 GB required92.2 GB available
50% VRAM used
Fit status
Runs well
Decode
17.9 tok/s
TTFT
10833 ms
Safe context
8K
Memory
45.7 GB / 92.2 GB
Memory breakdown
Weights28.8 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsFalcon 40B Instruct on Mac Studio M2 Ultra 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 17.9 tok/s decode · 10.8s TTFT (warm) · 45 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 | B | Runs well | 17.9 tok/s | 5909 ms | 8K |
| Coding | B | Runs well | 17.9 tok/s | 10833 ms | 8K |
| Agentic Coding | B | Runs well | 17.9 tok/s | 15757 ms | 8K |
| Reasoning | B | Runs well | 17.9 tok/s | 12803 ms | 8K |
| RAG | B | Runs well | 17.9 tok/s | 19696 ms | 8K |
Inference speed
Falcon 40B Instruct inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Falcon 40B Instruct at Q5_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 ~45 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 | Q5_K_M | 45.3 | Fits |
| 2× RTX 4090 24GB | 48 GB | Q5_K_M | 41.3 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q5_K_M | 35.3 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q5_K_M | 31.1 | Fits |
| RTX 5090 32GB | 32 GB | Q5_K_M | 29.4 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 23.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 23.1 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 21.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 17.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 16.9 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 12.9 | Offloads |
| RTX 4090 24GB | 24 GB | Q5_K_M | 10.5 | Too big |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 9.5 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 9.2 | Tight |
| RTX 3090 24GB | 24 GB | Q5_K_M | 9.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 8.5 | Tight |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 3.7 | Too big |
| RTX 4070 12GB | 12 GB | Q5_K_M | 2.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q5_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 Falcon 40B Instruct (40B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | B61 |
Q3_K_S | 3 | 19.6 GB | Low | B62 |
NVFP4 | 4 | 22.4 GB | Medium | B62 |
Q4_K_M | 4 | 24.4 GB | Medium | B62 |
Q5_K_M | 5 | 28.8 GB | High | B63 |
Q6_K | 6 | 32.8 GB | High | B64 |
Q8_0Best for your GPU | 8 | 42.8 GB | Very High | B66 |
F16 | 16 | 82.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run Falcon 40B Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "tiiuae/falcon-40b-instruct" \
--hf-file "falcon-40b-instruct-Q5_K_M.gguf" \
-c 4096 -ngl 99アップグレードオプション
Falcon 40B Instructを快適に動かすハードウェア
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run Falcon 40B Instruct?
Yes, Mac Studio M2 Ultra 128GB can run Falcon 40B Instruct with a B grade (Runs well). Expected decode speed: 17.9 tok/s.
How much VRAM does Falcon 40B Instruct need?
Falcon 40B Instruct (40B parameters) requires approximately 45.7 GB of memory with Q5_K_M quantization.
What is the best quantization for Falcon 40B Instruct?
The recommended quantization for Falcon 40B Instruct is Q5_K_M, which balances quality and memory efficiency.
What speed will Falcon 40B Instruct run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Falcon 40B Instruct achieves approximately 17.9 tokens per second decode speed with a time-to-first-token of 10833ms using Q5_K_M quantization.
Can Mac Studio M2 Ultra 128GB run Falcon 40B Instruct for coding?
For coding workloads, Falcon 40B Instruct on Mac Studio M2 Ultra 128GB receives a B grade with 17.9 tok/s and 8K context.
What context window can Falcon 40B Instruct use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Falcon 40B Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Falcon 40B Instruct?
Not always. Mac Studio M2 Ultra 128GB 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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