ERNIE-4.5-300B-A47B 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
Smallest card that fits
B200
180 GB · IQ4_XS · 154 tok/s
Fastest card
B200
154 tok/s · 180 GB
Which GPUs can run ERNIE-4.5-300B-A47B?
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.
7 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
154
tok/s
92–247 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 153.0 GB | IQ4_XS | Tight |
|
91.2
tok/s
55–146 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 240.3 GB | Q6_K | Tight |
|
75.2
tok/s
45–120 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 170.5 GB | Q4_K_M | Tight |
|
75.2
tok/s
45–120 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 170.5 GB | Q4_K_M | Tight |
|
72.8
tok/s
44–116 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 240.3 GB | Q6_K | Tight |
|
72.8
tok/s
44–116 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 240.3 GB | Q6_K | Tight |
|
65.6
tok/s
39–105 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 205.4 GB | Q5_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
- Baidu
- Organisation type
- Industry
- Country
- China
- Published
- 29 June 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Quantitative reasoning, Code generation, Translation, Question answering
- Numerical format
- FP8
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
- 300B
- Training data
- tokens
MoE total parameters: 300B active parameters: 47B
"We commence with large-scale pre-training on trillions of pure-text tokens sourced from diverse domains."
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
- 2.8 × 10²⁴ FLOP
They say the model was trained on "trillions of tokens" Speculatively assuming 10T tokens: 6 FLOP / token / parameter * 47 * 10^9 active parameters * 10 * 10^12 assumed training tokens = 2.82e+24 FLOP
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 H800 SXM5
- Hardware utilisation
- MFU 47.0%
Abstract says: "We achieve 47% Model FLOPs Utilization (MFU) during the pre-training of our largest ERNIE 4.5 language model." Page 4: "We efficiently pre-train ERNIE 4.5 using a heterogeneous hybrid parallelism approach and a hierarchical load balancing solution tailored to multimodal large models. Through our extreme optimizations, including efficient intra-node expert parallelism, FP8 mixed-precision training, and fine-grained recomputation methods, we achieve 47% Model FLOPs Utilization (MF…
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
- baidu
Apache 2.0 https://huggingface.co/baidu/ERNIE-4.5-300B-A47B-Base-PT Apache 2.0 for inference code https://github.com/PaddlePaddle/ERNIE
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- ERNIE 4.5 Technical Report
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs for ERNIE-4.5-300B-A47B
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 B200 180 GB · 8,000 GB/s · IQ4_XS 154 tok/s
- 02 B300 288 GB · 8,000 GB/s · Q6_K 91.2 tok/s
- 03 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q4_K_M 75.2 tok/s
- 04 Radeon Instinct MI308X 192 GB · 5,325 GB/s · Q4_K_M 75.2 tok/s
- 05 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q6_K 72.8 tok/s
- 06 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q6_K 72.8 tok/s
- 07 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q5_K_M 65.6 tok/s
The smallest GPUs that still run ERNIE-4.5-300B-A47B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 B200 180 GB · needs 153.0 GB · IQ4_XS · tight 154 tok/s
- 02 Radeon Instinct MI300X 192 GB · needs 170.5 GB · Q4_K_M · tight 75.2 tok/s
- 03 Radeon Instinct MI308X 192 GB · needs 170.5 GB · Q4_K_M · tight 75.2 tok/s
- 04 Radeon Instinct MI325X 256 GB · needs 205.4 GB · Q5_K_M · tight 65.6 tok/s
- 05 B300 288 GB · needs 240.3 GB · Q6_K · tight 91.2 tok/s
- 06 Radeon Instinct MI350X 288 GB · needs 240.3 GB · Q6_K · tight 72.8 tok/s
- 07 Radeon Instinct MI355X 288 GB · needs 240.3 GB · Q6_K · tight 72.8 tok/s
What the numbers mean
The hardware side
Minimum card
B200
Memory needed
153.0 GB
Fastest
154 tok/s
At 300B parameters, ERNIE-4.5-300B-A47B is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 7 of the cards we track can hold it on their own, and all of them are datacentre parts.
At the low end, a B200 handles it — 180 GB, at IQ4_XS, for about 154 tokens per second.
A B200 is the fastest we calculate for it: about 154 tokens per second, from 8,000 GB/s of memory bandwidth.
What this model is
ERNIE-4.5-300B-A47B was published by Baidu, in China, in June 2025. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Quantitative reasoning, Code generation, Translation, Question answering.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the baidu organisation on Hugging Face.
What decides the speed
The median result is around 75.2 tokens per second; 7 cards produce text faster than most people read it.
Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.
Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.
How it was trained
Producing it required around 2.8 × 10²⁴ FLOP of arithmetic, on NVIDIA H800 SXM5, which is a statement about the training budget rather than about inference.
Step by step
How to choose a GPU for ERNIE-4.5-300B-A47B
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Check what it needs before anything else
Look at what ERNIE-4.5-300B-A47B actually needs — around 153.0 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
-
02
Match the context to your actual use
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context ERNIE-4.5-300B-A47B can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of ERNIE-4.5-300B-A47B — IQ4_XS on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
The speed ordering for ERNIE-4.5-300B-A47B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 154 tok/s.
-
05
Check the fit verdict before buying
A tight fit runs ERNIE-4.5-300B-A47B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
-
06
See what else that card runs
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for ERNIE-4.5-300B-A47B alone — a card is usually bought for more than one model.
Answers
ERNIE-4.5-300B-A47B — common questions
When was ERNIE-4.5-300B-A47B released?
ERNIE-4.5-300B-A47B was published in June 2025.
What is ERNIE-4.5-300B-A47B used for?
ERNIE-4.5-300B-A47B works in Language, and is recorded as handling language modeling/generation, Quantitative reasoning, Code generation, Translation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download ERNIE-4.5-300B-A47B?
Its weights are published under the baidu organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train ERNIE-4.5-300B-A47B?
Around 2.8 × 10²⁴ FLOP, on NVIDIA H800 SXM5. 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.
Can I run ERNIE-4.5-300B-A47B if it does not fit in my GPU?
Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 43.6 GB. Our figures for ERNIE-4.5-300B-A47B assume it is fully resident.
Would two GPUs run ERNIE-4.5-300B-A47B faster?
Capacity adds across cards; throughput does not. Since 7 of the cards we track already hold ERNIE-4.5-300B-A47B on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for ERNIE-4.5-300B-A47B?
Each card is shown running the least-compressed copy it can hold, and ERNIE-4.5-300B-A47B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these ERNIE-4.5-300B-A47B speed estimates?
These are estimates with real error bars. The fastest result here, 92–247 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.
What GPU do I need to run ERNIE-4.5-300B-A47B?
The smallest card in our catalogue that holds ERNIE-4.5-300B-A47B is the B200, with 180 GB of memory. It runs the model at IQ4_XS using about 153.0 GB, and produces roughly 154 tokens per second. 7 cards in total can run it.
How fast is ERNIE-4.5-300B-A47B on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 154 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 7 of the cards that can run ERNIE-4.5-300B-A47B clear that.
How much VRAM does ERNIE-4.5-300B-A47B need?
About 153.0 GB at IQ4_XS compression, 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.
Is ERNIE-4.5-300B-A47B open source?
Its weights are published, so ERNIE-4.5-300B-A47B 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.
How many parameters does ERNIE-4.5-300B-A47B have?
ERNIE-4.5-300B-A47B has 300B parameters. MoE total parameters: 300B active parameters: 47B. 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.
Who created ERNIE-4.5-300B-A47B?
ERNIE-4.5-300B-A47B was published by Baidu, based in China, categorised as industry.
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.