Falcon 40B Instruct needs ~44.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q5_K_M quantization, expect ~78 tok/s.
Operating mode
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
Fit status
Runs well
Decode
77.6 tok/s
TTFT
2493 ms
Safe context
8K
Memory
44.6 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 77.6 tok/s | 1360 ms | 8K |
| Coding | B | Runs well | 77.6 tok/s | 2493 ms | 8K |
| Agentic Coding | B | Runs well | 77.6 tok/s | 3627 ms | 8K |
| Reasoning | B | Runs well | 77.6 tok/s | 2947 ms | 8K |
| RAG | B | Runs well | 77.6 tok/s | 4533 ms | 8K |
Inference speed
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.
How Falcon 40B Instruct (40B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | B59 |
Q3_K_S | 3 | 19.6 GB | Low | B60 |
NVFP4 | 4 | 22.4 GB | Medium | B60 |
Q4_K_M | 4 | 24.4 GB | Medium | B60 |
Q5_K_M | 5 | 28.8 GB | High | B61 |
Q6_K | 6 | 32.8 GB | High | B62 |
Q8_0 | 8 | 42.8 GB | Very High | B63 |
F16Best for your GPU | 16 | 82.0 GB | Maximum | B68 |
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
NVIDIA H200 141GBコスパ重視Raises estimated decode speed by about 100%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
〜$30,000 MSRP
NVIDIA H200 PCIe 141GBコスパ最良Raises estimated decode speed by about 100%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
〜$30,000 MSRP
Yes, Intel Data Center GPU Max 1550 128GB can run Falcon 40B Instruct with a B grade (Runs well). Expected decode speed: 77.6 tok/s.
Falcon 40B Instruct (40B parameters) requires approximately 44.6 GB of memory with Q5_K_M quantization.
The recommended quantization for Falcon 40B Instruct is Q5_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Falcon 40B Instruct achieves approximately 77.6 tokens per second decode speed with a time-to-first-token of 2493ms using Q5_K_M quantization.
For coding workloads, Falcon 40B Instruct on Intel Data Center GPU Max 1550 128GB receives a B grade with 77.6 tok/s and 8K context.
On Intel Data Center GPU Max 1550 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.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Paste this snippet into any page to show a live fit card.
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