Falcon 40B Instruct needs ~27.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With NVFP4 quantization, expect ~7 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
10.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.5 tok/s
TTFT
55081 ms
Safe context
4K
Memory
34.2 GB / 24.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.7 tok/s | 28428 ms | 4K |
| Coding | F | Too heavy | 3.5 tok/s | 55081 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 89104 ms | 4K |
| Reasoning | F | Too heavy | 3.5 tok/s | 65096 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 111380 ms | 4K |
How Falcon 40B Instruct (40B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 15.6 GB | Low | A70 |
Q3_K_S | 3 | 19.6 GB | Low | F0 |
NVFP4 | 4 | 22.4 GB | Medium | F0 |
Q4_K_M | 4 | 24.4 GB | Medium | F0 |
Q5_K_M | 5 | 28.8 GB | High | F0 |
Q6_K | 6 | 32.8 GB | High | F0 |
Q8_0 | 8 | 42.8 GB | Very High | F0 |
F16 | 16 | 82.0 GB | Maximum | F0 |
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 99Upgrade-Optionen
RTX PRO 5000 Blackwell 48GBGrößter SprungMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $4,999 MSRP
Gaudi 3 128GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $15,000 MSRP
Intel Data Center GPU Max 1550 128GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $15,000 MSRP
Yes, Intel Arc Pro B60 24GB can run Falcon 40B Instruct at NVFP4 quantization (Very compromised (needs ~3.1 GB host RAM)). The recommended Q5_K_M requires 34.2 GB which exceeds available memory, but at NVFP4 it needs only 27.8 GB. Expected decode speed: 7.1 tok/s.
Falcon 40B Instruct (40B parameters) requires approximately 34.2 GB at Q5_K_M quantization. On Intel Arc Pro B60 24GB, it fits at NVFP4 using 27.8 GB.
The recommended quantization is Q5_K_M, but on Intel Arc Pro B60 24GB the best fitting quantization is NVFP4, which uses 27.8 GB.
On Intel Arc Pro B60 24GB, Falcon 40B Instruct achieves approximately 7.1 tokens per second decode speed with a time-to-first-token of 27283ms using NVFP4 quantization.
For coding workloads, Falcon 40B Instruct on Intel Arc Pro B60 24GB receives a F grade with 3.5 tok/s and 4K context.
On Intel Arc Pro B60 24GB, Falcon 40B Instruct can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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