Calculate the TPS of the Tesla V100 DGXS 16 GB on local AI models
Every model in our catalogue assessed against this card at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from this card's memory bandwidth and the size of each model once compressed.
Calculated for this card
679 models in our catalogue altogether
Largest model it holds
Nemotron 3-Nano-30B-A3B
31.6B · Q3_K_M · 180 tok/s
Fastest model
Gemma 3 QAT 1B
380 tok/s · 1B
Which AI models can run on a Tesla V100 DGXS 16 GB?
Set the inputs, read the answer
More context means more memory for the conversation cache. Speed is for a fresh conversation and does not change with this setting.
Hides models that would only fit by being compressed below this point.
432 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
380
tok/s
323–456 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
380
tok/s
323–456 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
380
tok/s
228–608 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
380
tok/s
228–608 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
380
tok/s
228–608 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
380
tok/s
228–608 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
352
tok/s
211–563 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
345
tok/s
207–553 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
345
tok/s
207–553 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
345
tok/s
207–553 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
345
tok/s
207–553 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
309
tok/s
263–371 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
305
tok/s
183–487 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
292
tok/s
175–468 · low confidence |
Phi-1 ≈ | 1.3B | Oct 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
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
Tesla V100 DGXS 16 GB full specification
Everything on record for this board, ordered by how much it bears on running a language model rather than by how a spec sheet would list it. Memory comes first because it decides the outcome; the rest is context.
Memory
The two specifications that decide what this card can run and how quickly. Capacity sets which models fit; bandwidth sets how many tokens per second they produce once they do.
- Memory size
- 16 GB
- Memory bandwidth
- 897 GB/s
- Memory type
- HBM2
- Memory bus width
- 4,096 bit
- Memory clock
- 876 MHz
The chip
Which processor is on the board and how it was manufactured. A smaller process size generally means more performance for the same power.
- Graphics processor
- GV100
- Architecture
- Volta
- Generation
- Tesla Volta(Vxx)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 21.1 billion
- Transistor density
- 25,900 K/mm²
- Die size
- 815 mm²
- Released
- 27 March 2018
Clock speeds
How fast the processor runs. Worth far less here than on a gaming benchmark: generating text is limited by memory bandwidth, so a higher clock barely moves the result.
- Base clock
- 1.35 GHz
- Boost clock
- 1.58 GHz
Processing units
What the chip contains. These drive graphics performance and matter mainly for processing a long prompt rather than for producing the answer.
- Shading units
- 5,120
- Texture mapping units
- 320
- Render output units
- 128
- Streaming multiprocessors
- 80
- Tensor cores
- 640
- L1 cache
- 128 KB
- L2 cache
- 6 MB
Theoretical performance
Peak arithmetic rates published for the board. These are ceilings that no real workload reaches, and generating text reaches a small fraction of them because it is limited by memory rather than arithmetic.
- Half precision (FP16)
- 32.4 TFLOPS
- Single precision (FP32)
- 16.2 TFLOPS
- Double precision (FP64)
- 8.1 TFLOPS
- Pixel rate
- 203 GPixel/s
- Texture rate
- 506 GTexel/s
The board
What it takes to physically install and power the card — the practical constraints that decide whether it fits the machine you already own.
- Power draw (TDP)
- 250 W
- Suggested power supply
- 600 W
- Power connectors
- None
- Bus interface
- PCIe 3.0 x16
- Slot width
- Dual-slot
Software support
Which graphics and compute interfaces the card supports. CUDA compute capability is the one that bears on inference: below 7.0 there are no tensor cores, and modern inference software falls back to slower code paths.
- CUDA compute capability
- 7.0
- DirectX
- 12.1
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a Tesla V100 DGXS 16 GB
No vendor is currently listing this card. Listings come from vendors who publish them here directly — browse the vendor directory to see who is selling what.
What the numbers mean
Why memory is the number that matters here
Memory
16 GB
Bandwidth
897 GB/s
Largest model
Nemotron 3-Nano-30B-A3B
16 GB of HBM2 puts the Tesla V100 DGXS 16 GB comfortably into small and mid-sized models, with roughly 14.4 GB usable once the driver overhead is taken out. The largest models are out of reach without splitting them.
Bandwidth is 897 GB/s across a 4,096-bit bus. Generating a token means reading every weight once, so that figure sets the pace more than any other number here, and at this level text arrives faster than most people read.
That comes from a 876 MHz memory clock across the bus width above. Widening the bus and raising the clock are the two levers a manufacturer has, which is why a card with unremarkable cores can still generate quickly.
The biggest thing it holds is Nemotron 3-Nano-30B-A3B (31.6B) at Q3_K_M compression, for about 180 tokens per second.
The chip and how it was built
The Tesla V100 DGXS 16 GB is built on the GV100 graphics processor, using NVIDIA's Volta architecture, as part of the Tesla Volta(Vxx) generation.
The chip is manufactured by TSMC, on a 12 nm process, with a die measuring 815 mm², holding 21.1 billion transistors. A smaller process generally means more performance for the same power, though for language models it matters far less than the memory subsystem.
It was released in March 2018, roughly 8 years ago. Inference software support tends to follow hardware by a year or two, so a card of this age generally has mature, well-optimised code paths available to it.
Compute throughput, and why it matters less than it looks
FP16
32.4 TFLOPS
FP64
8.1 TFLOPS
Tensor cores
640
On paper the Tesla V100 DGXS 16 GB reaches 32.4 TFLOPS at half precision and 16.2 TFLOPS at single precision. These are peak figures no real workload sustains, and generating text reaches only a small fraction of them — decoding is limited by memory rather than arithmetic, which is why a card can look enormously powerful here and still produce tokens at an ordinary rate.
Double-precision throughput is 8.1 TFLOPS. It has no bearing on running a language model — no inference runtime uses it — but it separates datacentre parts from consumer ones, since the latter deliberately restrict it.
The card carries 640 tensor cores across 80 streaming multiprocessors. These accelerate the matrix arithmetic at the heart of a transformer, and they are what make prompt processing — reading a long document before answering — dramatically faster than it would otherwise be.
Clocks run from 1.35 GHz at base to 1.58 GHz boosted. Worth far less here than on a gaming benchmark: raising the clock speeds up the arithmetic, and the arithmetic is not what generation is waiting on.
Cache and processing units
The Tesla V100 DGXS 16 GB has 128 KB of L1 cache, backed by 6 MB of L2. Cache absorbs a share of the memory traffic that would otherwise hit the main bus, which is the one place on this page where a number other than bandwidth quietly affects generation speed — a large L2 lets more of the working set stay close to the cores.
There are 5,120 shading units, 320 texture mapping units, and 128 render output units. These drive graphics workloads and contribute to prompt processing, but they sit idle for much of the time a model spends generating a reply.
Power, size and installation
Power draw
250 W
The Tesla V100 DGXS 16 GB is rated at 250 W, with a 600 W power supply suggested for the whole system. Running a language model keeps a card busy in bursts rather than continuously — it draws hard while generating and idles between requests — so sustained draw over a working day is usually well below the rated figure.
The board occupies a dual-slot. Worth checking against the case and power supply already in the machine, since the largest cards need considerably more of both than a typical desktop provides.
It connects over PCIe 3.0 x16. The interface governs how quickly a model is loaded from disk into the card, not how fast it runs once there, so a narrower link costs a few seconds at startup and nothing thereafter.
The extremes
The largest AI models that run on a Tesla V100 DGXS 16 GB
The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.
The fastest AI models on a Tesla V100 DGXS 16 GB
Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.
Step by step
How to work out the tokens per second of a Tesla V100 DGXS 16 GB
You do not have to calculate anything by hand — the gputps.com calculator on this page has already worked it out for every model this card can hold. Reading off the answer takes six steps.
-
01
Search for the model you want
The table lists 432 models this Tesla V100 DGXS 16 GB can run. Search by name, or by size — typing 27b matches on the parameter count even when the name never states it.
-
02
Decide how long your conversations run
Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and on 16 GB it is often what pushes a large model over the edge.
-
03
Pin the comparison to one quality level
Compression is what lets bigger models fit. The quality control drops any model that needs more of it than you are willing to give.
-
04
Read the speed and the range
Speeds come with error bars for a reason. The best case here is 380 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.
-
05
Check the headroom before you decide
Compare what each model needs with the 16 GB this card provides. Tight means it works today; comfortable means it still works when the conversation grows.
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06
Open the model to compare cards
Following a model through to its own page lists all the hardware that can run it, so you can see where the Tesla V100 DGXS 16 GB sits against the alternatives.
Answers
Tesla V100 DGXS 16 GB — common questions
Can a Tesla V100 DGXS 16 GB run a 7B model?
Yes. For example a Tesla V100 DGXS 16 GB runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 56.7 tokens per second.
Can a Tesla V100 DGXS 16 GB run a 13B model?
Yes. For example a Tesla V100 DGXS 16 GB runs DeepSeekMoE-16B at Q6_K, using about 13.7 GB of memory and generating around 192 tokens per second.
Can a Tesla V100 DGXS 16 GB run a 30B model?
Yes. For example a Tesla V100 DGXS 16 GB runs ERNIE-4.5-VL-28B-A3B at Q3_K_M, using about 12.9 GB of memory and generating around 203 tokens per second.
How much memory does a Tesla V100 DGXS 16 GB have?
A Tesla V100 DGXS 16 GB has 16 GB of HBM2 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 14.4 GB available for a model and its conversation.
What is the memory bandwidth of a Tesla V100 DGXS 16 GB?
The Tesla V100 DGXS 16 GB has 897 GB/s of memory bandwidth, across a 4,096-bit memory bus. This is the single best predictor of how fast it generates text, because producing each token means reading the entire model out of memory once.
What type of memory does a Tesla V100 DGXS 16 GB use?
It uses HBM2 clocked at 876 MHz. HBM types are found on datacentre accelerators and carry far more bandwidth than the GDDR used on desktop cards, which is why they generate tokens considerably faster at the same capacity.
Who makes the Tesla V100 DGXS 16 GB?
The Tesla V100 DGXS 16 GB is a NVIDIA product, with the chip manufactured by TSMC, on a 12 nm process.
When was the Tesla V100 DGXS 16 GB released?
The Tesla V100 DGXS 16 GB was released in March 2018.
How much power does a Tesla V100 DGXS 16 GB use?
The Tesla V100 DGXS 16 GB has a rated board power of 250 W, and a 600 W system power supply is suggested. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.
How much cache does a Tesla V100 DGXS 16 GB have?
The Tesla V100 DGXS 16 GB has 128 KB of L1 cache, and 6 MB of L2 cache. Cache absorbs part of the memory traffic that would otherwise reach the main bus, so a larger L2 gives a modest lift to generation speed beyond what bandwidth alone predicts.
What are the TFLOPS of a Tesla V100 DGXS 16 GB?
The Tesla V100 DGXS 16 GB is rated at 32.4 TFLOPS at half precision and 16.2 TFLOPS at single precision. These are peak arithmetic ceilings rather than achievable rates, and text generation reaches only a small fraction of them because it is limited by memory bandwidth instead.
How many tensor cores does a Tesla V100 DGXS 16 GB have?
The Tesla V100 DGXS 16 GB has 640 tensor cores across 80 streaming multiprocessors. They accelerate the matrix arithmetic a transformer is built from, which mainly speeds up processing a long prompt rather than producing the reply.
Does the Tesla V100 DGXS 16 GB support CUDA?
Yes. The Tesla V100 DGXS 16 GB reports CUDA compute capability 7.0. Capability 7.0 and above has tensor cores, which modern inference software uses; below that it falls back to slower code paths for quantised models.
What bus interface does the Tesla V100 DGXS 16 GB use?
It uses PCIe 3.0 x16. This governs how fast a model is loaded onto the card rather than how fast it runs once loaded, so it costs a few seconds at startup and nothing during generation.
Is the Tesla V100 DGXS 16 GB good for running local AI models?
Its memory covers small and mid-sized models, though the largest are out of reach and its bandwidth is high enough to generate text faster than most people read. In total it runs 432 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a Tesla V100 DGXS 16 GB run a model that does not fit in its memory?
Only partly. Layers beyond the 16 GB sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. Every figure here assumes it is fully resident on the card.
Would two Tesla V100 DGXS 16 GB cards be twice as fast?
No. A second Tesla V100 DGXS 16 GB doubles the memory to 32 GB, which lets you hold models neither could hold alone, but generation does not split that way. These figures describe one card.
What AI models can a Tesla V100 DGXS 16 GB run?
432 of the 679 open-weight language models we track fit on a Tesla V100 DGXS 16 GB and can be run locally on it. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.
What is the largest AI model a Tesla V100 DGXS 16 GB can run?
The largest model in our catalogue that fits on a Tesla V100 DGXS 16 GB is Nemotron 3-Nano-30B-A3B at 31.6B parameters, compressed to Q3_K_M. It generates roughly 180 tokens per second and needs about 14.4 GB of the card's memory.
How many tokens per second does a Tesla V100 DGXS 16 GB produce?
It depends on the model. On a Tesla V100 DGXS 16 GB the fastest model we track is Gemma 3 QAT 1B at about 380 tokens per second, while larger models run proportionally slower because each token requires reading the whole model out of memory once. Speeds are estimates for a single conversation at a time.
The other direction
Looking at it from the other side?
This page starts from the hardware. If you already know which model you want and need to know what it takes to run it, start from the model instead.