Can Falcon 40B Instruct run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B69Good

○Estimated from fit model

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 ~71 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 44.6 GB, 77.6 tok/s, Runs well

44.6 GB required128.0 GB available

35% VRAM used

Fit status

Runs well

Decode

77.6 tok/s

TTFT

2493 ms

Safe context

Memory

44.6 GB / 128.0 GB

Memory breakdown

Weights28.8 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsFalcon 40B Instruct on Intel Data Center GPU Max 1550 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: 77.6 tok/s decode · 2.5s TTFT (warm) · 194 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	77.6 tok/s	1360 ms	8K
Coding	B	Runs well	71.4 tok/s	2711 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

Quantization options

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

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

Upgrade options

Hardware that runs Falcon 40B Instruct well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.155.3 tok/s decode

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
NVIDIA H200 PCIe 141GBBest value