Can Falcon 40B Instruct run on Radeon Pro W6800 32GB?

BARELY — Tight on Memory

B56Good
Estimated from fit model

Falcon 40B Instruct needs ~35.0 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q5_K_M quantization, expect ~6 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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) 35.0 GB, 6.8 tok/s, Very compromised (needs ~2.5 GB host RAM)
35.0 GB required32.0 GB available
109% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.5 GB host RAM)

Decode

6.8 tok/s

TTFT

28280 ms

Safe context

4K

Memory

35.0 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights28.8 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsFalcon 40B Instruct on Radeon Pro W6800 32GB
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: 6.8 tok/s decode · 28.3s TTFT (warm) · 17 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload6.7 tok/s15866 ms4K
CodingBVery compromised6.3 tok/s30755 ms4K
Agentic CodingBVery compromised5.7 tok/s49799 ms4K
ReasoningBVery compromised6.3 tok/s36346 ms4K
RAGBVery compromised5.7 tok/s62248 ms4K

Quantization options

How Falcon 40B Instruct (40B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
15.6 GB
LowA70
Q3_K_S
3
19.6 GB
LowB70
NVFP4
4
22.4 GB
MediumB69
Q4_K_MBest for your GPU
4
24.4 GB
MediumB69
Q5_K_M
5
28.8 GB
HighF0
Q6_K
6
32.8 GB
HighF0
Q8_0
8
42.8 GB
Very HighF0
F16
16
82.0 GB
MaximumF0

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-Optionen

Hardware, die Falcon 40B Instruct gut ausführt

Frequently asked questions

Can Radeon Pro W6800 32GB run Falcon 40B Instruct?

Yes, Radeon Pro W6800 32GB can run Falcon 40B Instruct with a B grade (Very compromised). Expected decode speed: 6.3 tok/s.

How much VRAM does Falcon 40B Instruct need?

Falcon 40B Instruct (40B parameters) requires approximately 35.0 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 Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Falcon 40B Instruct achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30755ms using Q5_K_M quantization.

Can Radeon Pro W6800 32GB run Falcon 40B Instruct for coding?

For coding workloads, Falcon 40B Instruct on Radeon Pro W6800 32GB receives a B grade with 6.3 tok/s and 4K context.

What context window can Falcon 40B Instruct use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Falcon 40B Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Falcon 40B Instruct feels slow on Radeon Pro W6800 32GB?

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.

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