Falcon-H1 34B Instruct on NVIDIA RTX PRO 5000 Blackwell 48GB

Can the NVIDIA RTX PRO 5000 Blackwell 48GB run Falcon-H1 34B Instruct?

Yes, it runs great

Runs great. At Q5_K_M it needs about 28.1 GB of 48.0 GB usable, with an estimated speed of 40.5 tok/s.

Best quant: Q5_K_M
Generation speed: 40.5 tok/s
Prompt processing: 1,391 tok/s
First token (1K prompt): 0.7 s
Largest safe context: 139K

Best app to run it on the NVIDIA RTX PRO 5000 Blackwell 48GB

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

Point-and-click: search, download and chat in one window.

Open Falcon-H1 34B Instruct in LM Studio

llama.cpp Usually the first to support new model architectures.
Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Linux

vLLM

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve tiiuae/Falcon-H1-34B-Instruct

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Falcon-H1 34B Instruct

Recommended for the NVIDIA RTX PRO 5000 Blackwell 48GB: Q5_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q5_K_M file (opens in a new tab)

huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from TII, for fine-tuning, vLLM, Transformers.

huggingface.co/tiiuae/Falcon-H1-34B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from TII. The weights themselves download from Hugging Face.

github.com/tiiuae/Falcon-H1 (opens in a new tab)

git clone https://github.com/tiiuae/Falcon-H1

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 62Good quality (B) · 62 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q5_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great: How well it runs on the NVIDIA RTX PRO 5000 Blackwell 48GB: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	27.9 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	13.5 / 15
Match score	81.4 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA RTX PRO 5000 Blackwell 48GB

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	17.7 GB	48.0 GB	80.5 tok/s	Runs great	44Fair quality (C) · 44 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	20.7 GB	48.0 GB	63.4 tok/s	Runs great	53Fair quality (C) · 53 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	24.7 GB	48.0 GB	49.0 tok/s	Runs great	60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	28.1 GB	48.0 GB	40.5 tok/s	Runs great	62Good quality (B) · 62 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	31.8 GB	48.0 GB	34.0 tok/s	Runs great	63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	39.9 GB	48.0 GB	25.0 tok/s	Runs well	64Good quality (B) · 64 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	71.5 GB	48.0 GB	—	Won't run	64Good quality (B) · 64 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q6_K · 45.4 tok/s · Runs well (tight fit)
60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q4_K_M · 36.8 tok/s · Runs well (tight fit)
60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q4_K_M · 34.2 tok/s · Runs well (tight fit)
60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q4_K_M · 30.2 tok/s · Runs well (tight fit)
63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Q6_K · 45.4 tok/s · Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) Q4_K_M · 17.2 tok/s · Runs well

Quick answers

How much memory does Falcon-H1 34B Instruct need on the NVIDIA RTX PRO 5000 Blackwell 48GB?

At Q5_K_M it needs about 28.1 GB. The NVIDIA RTX PRO 5000 Blackwell 48GB has 48.0 GB usable.

How fast is Falcon-H1 34B Instruct on the NVIDIA RTX PRO 5000 Blackwell 48GB?

About 40.5 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Falcon-H1 34B Instruct should I use on the NVIDIA RTX PRO 5000 Blackwell 48GB?

Q5_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.