Will It Run AI

Can Falcon 40B Instruct run on NVIDIA DGX Spark 128GB?

YES — Runs Great

B63Good
Estimated from fit model

Falcon 40B Instruct needs ~44.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q5_K_M quantization, expect ~6 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
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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) 44.9 GB, 6.3 tok/s, Runs well
44.9 GB required108.8 GB available
41% VRAM used

Fit status

Runs well

Decode

6.3 tok/s

TTFT

30687 ms

Safe context

8K

Memory

44.9 GB / 108.8 GB

Memory breakdown

Weights28.8 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsFalcon 40B Instruct on NVIDIA DGX Spark 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: 6.3 tok/s decode · 30.7s TTFT (warm) · 16 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well6.3 tok/s16738 ms8K
CodingBRuns well6.3 tok/s30687 ms8K
Agentic CodingBRuns well6.3 tok/s44636 ms8K
ReasoningBRuns well6.3 tok/s36266 ms8K
RAGBRuns well6.3 tok/s55795 ms8K

Quantization options

How Falcon 40B Instruct (40B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
15.6 GB
LowB61
Q3_K_S
3
19.6 GB
LowB62
NVFP4
4
22.4 GB
MediumB62
Q4_K_M
4
24.4 GB
MediumB62
Q5_K_M
5
28.8 GB
HighB63
Q6_K
6
32.8 GB
HighB64
Q8_0Best for your GPU
8
42.8 GB
Very HighB66
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

升级选项

能流畅运行 Falcon 40B Instruct 的硬件

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Falcon 40B Instruct?

Yes, NVIDIA DGX Spark 128GB can run Falcon 40B Instruct with a B grade (Runs well). Expected decode speed: 6.3 tok/s.

How much VRAM does Falcon 40B Instruct need?

Falcon 40B Instruct (40B parameters) requires approximately 44.9 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 NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Falcon 40B Instruct achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30687ms using Q5_K_M quantization.

Can NVIDIA DGX Spark 128GB run Falcon 40B Instruct for coding?

For coding workloads, Falcon 40B Instruct on NVIDIA DGX Spark 128GB receives a B grade with 6.3 tok/s and 8K context.

What context window can Falcon 40B Instruct use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 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.

What should I upgrade first if Falcon 40B Instruct feels slow on NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Falcon 40B Instruct?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GBSee all hardware for Falcon 40B Instruct
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