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Can Falcon 40B Instruct run on AMD Instinct MI350X 288GB?

YES — Runs Great

B67Good
Estimated from fit model

Falcon 40B Instruct needs ~60.6 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q5_K_M quantization, expect ~225 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) 60.6 GB, 224.9 tok/s, Runs well
60.6 GB required288.0 GB available
21% VRAM used

Fit status

Runs well

Decode

224.9 tok/s

TTFT

861 ms

Safe context

8K

Memory

60.6 GB / 288.0 GB

Memory breakdown

Weights28.8 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom28.8 GB

See how fast it feels

See how fast it feelsFalcon 40B Instruct on AMD Instinct MI350X 288GB
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: 224.9 tok/s decode · 861ms TTFT (warm) · 562 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well224.9 tok/s469 ms8K
CodingBRuns well224.9 tok/s861 ms8K
Agentic CodingBRuns well224.9 tok/s1252 ms8K
ReasoningBRuns well224.9 tok/s1017 ms8K
RAGBRuns well224.9 tok/s1565 ms8K

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 / MacMemoryQuantSpeed (tok/s)Fits?
2× RX 7900 XTX 24GB
48 GBQ5_K_M45.3Fits
NVIDIA2× RTX 4090 24GB
48 GBQ5_K_M41.3Fits
NVIDIA2× RTX 3090 24GB
48 GBQ5_K_M35.3Fits
NVIDIA4× RTX 3060 12GB
48 GBQ5_K_M31.1Fits
NVIDIARTX 5090 32GB
32 GBQ5_K_M29.4Heavy offload
MacBook Pro M4 Max 128GB
128 GBQ5_K_M23.1Fits
MacBook Pro M4 Max 64GB
64 GBQ5_K_M23.1Tight
Mac Studio M3 Ultra 256GB
256 GBQ5_K_M21.5Fits
Mac Studio M2 Ultra 128GB
128 GBQ5_K_M17.9Fits
Mac Studio M1 Ultra 128GB
128 GBQ5_K_M16.9Fits
MacBook Pro M4 Pro 48GB
48 GBQ5_K_M12.9Offloads
NVIDIARTX 4090 24GB
24 GBQ5_K_M10.5Too big
RX 7900 XTX 24GB
24 GBQ5_K_M9.5Too big
MacBook Pro M3 Max 64GB
64 GBQ5_K_M9.2Tight
NVIDIARTX 3090 24GB
24 GBQ5_K_M9.0Too big
MacBook Pro M1 Max 64GB
64 GBQ5_K_M8.5Tight
NVIDIARTX 4080 Super 16GB
16 GBQ5_K_M3.7Too big
NVIDIARTX 4070 12GB
12 GBQ5_K_M2.2Too big
NVIDIARTX 3060 12GB
12 GBQ5_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ5_K_M2.0Too 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 AMD Instinct MI350X 288GB (288.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
15.6 GB
LowB57
Q3_K_S
3
19.6 GB
LowB57
NVFP4
4
22.4 GB
MediumB58
Q4_K_M
4
24.4 GB
MediumB58
Q5_K_M
5
28.8 GB
HighB58
Q6_K
6
32.8 GB
HighB58
Q8_0
8
42.8 GB
Very HighB59
F16Best for your GPU
16
82.0 GB
MaximumB62

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

Frequently asked questions

Can AMD Instinct MI350X 288GB run Falcon 40B Instruct?

Yes, AMD Instinct MI350X 288GB can run Falcon 40B Instruct with a B grade (Runs well). Expected decode speed: 224.9 tok/s.

How much VRAM does Falcon 40B Instruct need?

Falcon 40B Instruct (40B parameters) requires approximately 60.6 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Falcon 40B Instruct achieves approximately 224.9 tokens per second decode speed with a time-to-first-token of 861ms using Q5_K_M quantization.

Can AMD Instinct MI350X 288GB run Falcon 40B Instruct for coding?

For coding workloads, Falcon 40B Instruct on AMD Instinct MI350X 288GB receives a B grade with 224.9 tok/s and 8K context.

What context window can Falcon 40B Instruct use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, 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.

See all results for AMD Instinct MI350X 288GBSee all hardware for Falcon 40B Instruct
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