Can Falcon 40B Instruct run on RTX 5080 Laptop 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Falcon 40B Instruct needs ~33.4 GB but RTX 5080 Laptop 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 33.4 GB, exceeds 16.0 GB available

33.4 GB required16.0 GB available

209% VRAM needed

17.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.0 tok/s

TTFT

49004 ms

Safe context

Memory

33.4 GB / 16.0 GB

Offload

50%

Memory breakdown

Weights28.8 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsFalcon 40B Instruct on RTX 5080 Laptop 16GB
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: 4.0 tok/s decode · 49.0s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 33.4 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.2 tok/s	25212 ms	4K
Coding	F	Too heavy	4.0 tok/s	49004 ms	4K
Agentic Coding	F	Too heavy	3.7 tok/s	75555 ms	4K
Reasoning	F	Too heavy	4.0 tok/s	57914 ms	4K
RAG	F	Too heavy	3.7 tok/s	94444 ms	4K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q5_K_M	45.3	Fits
2× RTX 4090 24GB	48 GB	Q5_K_M	41.3	Fits
2× RTX 3090 24GB	48 GB	Q5_K_M	35.3	Fits
4× RTX 3060 12GB	48 GB	Q5_K_M	31.1	Fits
RTX 5090 32GB	32 GB	Q5_K_M	29.4	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	23.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	23.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	21.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	17.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	16.9	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	12.9	Offloads
RTX 4090 24GB	24 GB	Q5_K_M	10.5	Too big
RX 7900 XTX 24GB	24 GB	Q5_K_M	9.5	Too big
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	9.2	Tight
RTX 3090 24GB	24 GB	Q5_K_M	9.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	8.5	Tight
RTX 4080 Super 16GB	16 GB	Q5_K_M	3.7	Too big
RTX 4070 12GB	12 GB	Q5_K_M	2.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.0	Too 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 RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	15.6 GB	Low	F0
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

Opciones de mejora

Hardware que ejecuta bien Falcon 40B Instruct

RTX 5090 32GBMejor relación calidad-precio

32 GB VRAM (+16)1792 GB/s (+1024)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.29.4 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 635%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejora NVIDIA

32 GB VRAM (+16)896 GB/s (+128)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.18.4 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 360%.

~$2,499 MSRP

RTX A6000 48GBOpción económica

48 GB VRAM (+32)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.22.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$4,650 MSRP

Frequently asked questions

Can RTX 5080 Laptop 16GB run Falcon 40B Instruct?

No, Falcon 40B Instruct requires more memory than RTX 5080 Laptop 16GB provides.

How much VRAM does Falcon 40B Instruct need?

Falcon 40B Instruct (40B parameters) requires approximately 33.4 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 RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Falcon 40B Instruct achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 49004ms using Q5_K_M quantization.

Can RTX 5080 Laptop 16GB run Falcon 40B Instruct for coding?

For coding workloads, Falcon 40B Instruct on RTX 5080 Laptop 16GB receives a F grade with 4.0 tok/s and 4K context.

What context window can Falcon 40B Instruct use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, 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 RTX 5080 Laptop 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5080 Laptop 16GB See all hardware for Falcon 40B Instruct

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