Nemotron-4 340B TPS calculator
Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.
Calculated for this model
818 cards we hold specifications for
Smallest card that fits
Radeon Instinct MI300X
192 GB · Q3_K_M · 14.0 tok/s
Fastest card
B300
17.8 tok/s · 288 GB
Which GPUs can run Nemotron-4 340B?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
6 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
17.8
tok/s
11–28 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 246.0 GB | Q5_K_M | Tight |
|
14.2
tok/s
9–23 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 246.0 GB | Q5_K_M | Tight |
|
14.2
tok/s
9–23 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 246.0 GB | Q5_K_M | Tight |
|
14.0
tok/s
8–22 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 166.8 GB | Q3_K_M | Tight |
|
14.0
tok/s
8–22 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 166.8 GB | Q3_K_M | Tight |
|
13.5
tok/s
8–22 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 206.4 GB | Q4_K_M | Tight |
Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- NVIDIA
- Organisation type
- Industry
- Country
- United States of America
- Published
- 14 June 2024
- Authors
- Bo Adler, Niket Agarwal, Ashwath Aithal, Dong H. Anh, Pallab Bhattacharya, Annika Brundyn, Jared Casper, Bryan Catanzaro, Sharon Clay, Jonathan Cohen, Sirshak Das, Ayush Dattagupta, Olivier Delalleau, Leon Derczynski, Yi Dong, Daniel Egert, Ellie Evans, Aleksander Ficek, Denys Fridman, Shaona Ghosh, Boris Ginsburg, Igor Gitman, Tomasz Grzegorzek, Robert Hero, Jining Huang, Vibhu Jawa, Joseph Jenni…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat, Question answering
- Numerical format
- BF16
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 340B
- Training data
- 9,000,000,000,000 tokens
- Batch size
- 9,437,184
340B
9T training tokens. They first train on an 8T token dataset and then an additional 1T tokens, it's slightly unclear if that's more data or a partial second epoch 6.75T words using 1 token = 0.75 words
2304 * 4096
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.8 × 10²⁵ FLOP
- How it was established
- Operation counting,Hardware
9 trillion tokens for training 6 * 340B * 9T = 1.8E25 alternatively, can do a hardware estimate with a few extra steps: According to the technical report, Nemotron-4 340B was trained using up to 6144 H100 GPUs. Helpfully, they also report the model FLOP utilization (MFU), which was 41-42% (Table 2). This is the ratio of the actual output of their GPUs, in FLOP used for training, relative to their theoretical max of 989 teraFLOP/s per GPU. Unfortunately, the report omits the last ingredient, w…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chips used
- 6,144
- Wall-clock time
- 2,200 hours (91.7 days)
- Hardware utilisation
- MFU 41.1%
- Power draw
- 8.5 MW
- Compute cost
- $21,271,018
see training compute notes, this is an inferred estimate
Table 2 indicates MFU at different stages of training. ((42.4% * 200B) + (42.3% * 200B) + (41.0% * 7600B)) / (200B + 200B + 7600B) = 0.410675, averaged over training.
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
- Hugging Face
- nvidia
Permissive commercial license: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Confident
~2e25 FLOP, so high training cost, likely >5M
Sources
Where this record came from and when it was last checked.
- Reference
- NVIDIA Releases Open Synthetic Data Generation Pipeline for Training Large Language Models
- Last updated
- 18 December 2025
The extremes
The ten fastest GPUs that run Nemotron-4 340B
Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.
- 01 B300 288 GB · 8,000 GB/s · Q5_K_M 17.8 tok/s
- 02 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q5_K_M 14.2 tok/s
- 03 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q5_K_M 14.2 tok/s
- 04 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q3_K_M 14.0 tok/s
- 05 Radeon Instinct MI308X 192 GB · 5,325 GB/s · Q3_K_M 14.0 tok/s
- 06 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q4_K_M 13.5 tok/s
The smallest GPUs that still run Nemotron-4 340B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 Radeon Instinct MI300X 192 GB · needs 166.8 GB · Q3_K_M · tight 14.0 tok/s
- 02 Radeon Instinct MI308X 192 GB · needs 166.8 GB · Q3_K_M · tight 14.0 tok/s
- 03 Radeon Instinct MI325X 256 GB · needs 206.4 GB · Q4_K_M · tight 13.5 tok/s
- 04 B300 288 GB · needs 246.0 GB · Q5_K_M · tight 17.8 tok/s
- 05 Radeon Instinct MI350X 288 GB · needs 246.0 GB · Q5_K_M · tight 14.2 tok/s
- 06 Radeon Instinct MI355X 288 GB · needs 246.0 GB · Q5_K_M · tight 14.2 tok/s
What the numbers mean
What it takes to run this model
Minimum card
Radeon Instinct MI300X
Memory needed
166.8 GB
Fastest
17.8 tok/s
Nemotron-4 340B reaches a parameter count of 340B. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 6.
The smallest card that holds it is Radeon Instinct MI300X, with a memory capacity of 192 GB, running it at a compression of Q3_K_M and producing around 14.0 tokens per second.
The fastest we calculate for it is B300, generating roughly 17.8 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.
What this model is
Nemotron-4 340B was published by NVIDIA, in the country recorded as United States of America, during June 2024. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Chat, Question answering.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation nvidia.
What decides the speed
Half the cards that hold it manage more than 14.1 tokens per second. Producing text faster than most people read it: 6 of them.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.
Training and provenance
The training run consumed about 1.8 × 10²⁵ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 9,000,000,000,000 tokens of text.
Its inclusion criterion: training cost.
Step by step
How to choose a GPU for Nemotron-4 340B
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Start from the memory column
Every card here has been checked against Nemotron-4 340B, needing around 166.8 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
-
02
Set the context length you will work at
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Nemotron-4 340B.
-
03
Set a quality floor
Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
-
04
Rank by throughput rather than spec sheet
The speed ordering is effectively an ordering by memory bandwidth, for Nemotron-4 340B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B300, at 17.8 tok/s.
-
05
Look at the headroom, not just the fit
A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Nemotron-4 340B. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
-
06
Open the card you have settled on
Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on Nemotron-4 340B.
Answers
Nemotron-4 340B — common questions
Nemotron-4 340B— can I run it if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. The nearest miss we calculate falls short by 44.4 GB. Every figure here assumes the whole model is resident on the card.
Nemotron-4 340B— would two GPUs run it faster?
Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 6. So a second card is rarely the answer here.
Nemotron-4 340B— why does the quantisation differ between cards?
Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Nemotron-4 340B— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: 11–28 tok/s on B300. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Nemotron-4 340B— what GPU do I need to run it?
The smallest card in our catalogue that holds it is Radeon Instinct MI300X, with a memory capacity of 192 GB. It runs the model at a compression of Q3_K_M using about 166.8 GB, and produces roughly 14.0 tokens per second. The number of cards able to run it in total: 6.
Nemotron-4 340B— how fast is it on a GPU?
It depends on the card. The quickest we calculate is B300, at about 17.8 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 6.
Nemotron-4 340B— how much VRAM does it need?
It needs about 166.8 GB at a compression of Q3_K_M, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.
Nemotron-4 340B— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
Nemotron-4 340B— how many parameters does it have?
It has a parameter count of 340B. 340B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Nemotron-4 340B— who created it?
It was published by NVIDIA, based in United States of America, an organisation categorised as industry.
Nemotron-4 340B— when was it released?
It was published in June 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Nemotron-4 340B— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Chat, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Nemotron-4 340B— where can I download it?
Its weights are published on Hugging Face, under the organisation nvidia. We do not host model files — this site calculates what hardware is needed to run them.
Nemotron-4 340B— how much compute was used to train it?
Training consumed around 1.8 × 10²⁵ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.