ERNIE-4.5-VL-424B-A47B (文心大模型4.5) 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
Radeon Instinct MI325X
256 GB · IQ4_XS · 63.8 tok/s
Fastest card
B300
102 tok/s · 288 GB
Which GPUs can run ERNIE-4.5-VL-424B-A47B (文心大模型4.5)?
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.
4 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
102
tok/s
62–164 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 235.4 GB | Q4_K_M | Tight |
|
81.8
tok/s
49–131 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 235.4 GB | Q4_K_M | Tight |
|
81.8
tok/s
49–131 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 235.4 GB | Q4_K_M | Tight |
|
63.8
tok/s
38–102 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 210.8 GB | IQ4_XS | 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
- 16 March 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Video
- Task
- Language modeling/generation, Visual question answering, Video description, Speech recognition (ASR), Quantitative reasoning, Code generation, Translation, Question answering, Character recognition (OCR)
- Base model
- ERNIE-4.5-300B-A47B
- 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
- 424B
- Training data
- tokens
MoE: total parameters - 424B active parameters - 47B "ViT encoder comprising 630 million parameters"
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-VL-424B-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
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
ERNIE 4.5 surpassed GPT-4o in six out of seven evaluated benchmarks: [Table 7, Table 8] CCBench: Evaluates common-sense reasoning across text and images. ERNIE 4.5 scored approximately 81, slightly outperforming GPT-4o’s ~79. OCRBench: Assesses optical character recognition capabilities, focusing on text extraction from images. ERNIE 4.5 achieved around 88, surpassing GPT-4o’s ~81. ChartQA: Tests understanding of data presented in charts. ERNIE 4.5 scored ~82, marginally ahead of GPT-4o’s ~81. M…
Sources
Where this record came from and when it was last checked.
- Reference
- Baidu's ERNIE 4.5 & X1: Features, Access, DeepSeek Comparison
- Last updated
- 11 February 2026
The extremes
The ten fastest GPUs for ERNIE-4.5-VL-424B-A47B (文心大模型4.5)
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.
The smallest GPUs that still run ERNIE-4.5-VL-424B-A47B (文心大模型4.5)
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
What the numbers mean
The hardware side
Minimum card
Radeon Instinct MI325X
Memory needed
210.8 GB
Fastest
102 tok/s
At 424B parameters, ERNIE-4.5-VL-424B-A47B (文心大模型4.5) is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 4 of the cards we track can hold it on their own, and all of them are datacentre parts.
At the low end, a Radeon Instinct MI325X handles it — 256 GB, at IQ4_XS, for about 63.8 tokens per second.
A B300 is the fastest we calculate for it: about 102 tokens per second, from 8,000 GB/s of memory bandwidth.
What this model is
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) was published by Baidu, in China, in March 2025. The organisation is categorised as industry.
It works in Multimodal, Language, Vision, Video, and is recorded as doing language modeling/generation, Visual question answering, Video description, Speech recognition (ASR), Quantitative reasoning, Code generation, Translation, Question answering, Character recognition (OCR).
It builds on ERNIE-4.5-300B-A47B, which is why it shares that model's general shape and size.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the baidu organisation on Hugging Face.
What decides the speed
Across every card that can run it, the middle of the range is about 81.8 tokens per second, and 4 of them clear the ten tokens per second that roughly matches reading speed.
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
The reason it appears in this catalogue at all is sOTA improvement.
Step by step
How to choose a GPU for ERNIE-4.5-VL-424B-A47B (文心大模型4.5)
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Read the memory figure first
Look at what ERNIE-4.5-VL-424B-A47B (文心大模型4.5) actually needs — around 210.8 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
-
02
Set the context length you will work at
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for ERNIE-4.5-VL-424B-A47B (文心大模型4.5).
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage ERNIE-4.5-VL-424B-A47B (文心大模型4.5) by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
Ranking by tokens per second for ERNIE-4.5-VL-424B-A47B (文心大模型4.5) follows memory bandwidth, not core counts, which is why the B300 tops it at 102 tok/s.
-
05
Look at the headroom, not just the fit
The fit column separates cards that just manage ERNIE-4.5-VL-424B-A47B (文心大模型4.5) from those with room to spare. Buy for the second if the context might grow.
-
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-VL-424B-A47B (文心大模型4.5) alone — a card is usually bought for more than one model.
Answers
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) — common questions
How much VRAM does ERNIE-4.5-VL-424B-A47B (文心大模型4.5) need?
About 210.8 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-VL-424B-A47B (文心大模型4.5) open source?
Its weights are published, so ERNIE-4.5-VL-424B-A47B (文心大模型4.5) 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-VL-424B-A47B (文心大模型4.5) have?
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) has 424B parameters. MoE: total parameters - 424B active parameters - 47B "ViT encoder comprising 630 million parameters". 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-VL-424B-A47B (文心大模型4.5)?
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) was published by Baidu, based in China, categorised as industry.
When was ERNIE-4.5-VL-424B-A47B (文心大模型4.5) released?
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) was published in March 2025.
What is ERNIE-4.5-VL-424B-A47B (文心大模型4.5) used for?
ERNIE-4.5-VL-424B-A47B (文心大模型4.5) works in Multimodal, Language, Vision, Video, and is recorded as handling language modeling/generation, Visual question answering, Video description, Speech recognition (ASR), Quantitative reasoning, Code generation, Translation, Question answering, Character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download ERNIE-4.5-VL-424B-A47B (文心大模型4.5)?
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.
Can I run ERNIE-4.5-VL-424B-A47B (文心大模型4.5) if it does not fit in my GPU?
It can be split between the card and system memory, but ERNIE-4.5-VL-424B-A47B (文心大模型4.5) generates painfully slowly that way — the nearest miss we calculate is short by 62.6 GB. Nothing on this page assumes offloading.
Would two GPUs run ERNIE-4.5-VL-424B-A47B (文心大模型4.5) faster?
A second card roughly doubles the memory available but not the generation rate. With 4 cards already able to run ERNIE-4.5-VL-424B-A47B (文心大模型4.5) alone, the case for pairing is weak.
Why does the quantisation differ between cards for ERNIE-4.5-VL-424B-A47B (文心大模型4.5)?
Each card is shown running the least-compressed copy it can hold, and ERNIE-4.5-VL-424B-A47B (文心大模型4.5) appears at 2 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these ERNIE-4.5-VL-424B-A47B (文心大模型4.5) speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 62–164 tok/s on the B300, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
What GPU do I need to run ERNIE-4.5-VL-424B-A47B (文心大模型4.5)?
The smallest card in our catalogue that holds ERNIE-4.5-VL-424B-A47B (文心大模型4.5) is the Radeon Instinct MI325X, with 256 GB of memory. It runs the model at IQ4_XS using about 210.8 GB, and produces roughly 63.8 tokens per second. 4 cards in total can run it.
How fast is ERNIE-4.5-VL-424B-A47B (文心大模型4.5) on a GPU?
It depends on the card. The quickest we calculate is a B300 at about 102 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 4 of the cards that can run ERNIE-4.5-VL-424B-A47B (文心大模型4.5) clear that.
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.