Calculate the TPS of the RTX 6000 Ada Generation 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
Largest model it holds
Qwen3-Coder-Next
80B · IQ4_XS · 69.3 tok/s
Fastest model
Gemma 3 QAT 1B
407 tok/s · 1B
What AI models can a RTX 6000 Ada Generation run?
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.
576 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
407
tok/s
346–488 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
407
tok/s
346–488 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
407
tok/s
244–651 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
407
tok/s
244–651 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
407
tok/s
244–651 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
407
tok/s
244–651 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
376
tok/s
226–602 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
370
tok/s
222–591 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
370
tok/s
222–591 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
370
tok/s
222–591 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
370
tok/s
222–591 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
339
tok/s
203–542 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
339
tok/s
203–542 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
339
tok/s
203–542 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
339
tok/s
203–542 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
331
tok/s
281–397 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
326
tok/s
196–522 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
313
tok/s
188–500 · 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
RTX 6000 Ada Generation 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
- 48 GB
- Memory bandwidth
- 960 GB/s
- Memory type
- GDDR6
- Memory bus width
- 384 bit
- Memory clock
- 2.5 GHz
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
- AD102
- Architecture
- Ada Lovelace
- Generation
- Workstation Ada(x000A)
- Foundry
- TSMC
- Process size
- 5 nm
- Transistors
- 76.3 billion
- Transistor density
- 125,300 K/mm²
- Die size
- 609 mm²
- Released
- 3 December 2022
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
- 915 MHz
- Boost clock
- 2.51 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
- 18,176
- Texture mapping units
- 568
- Render output units
- 192
- Streaming multiprocessors
- 142
- Tensor cores
- 568
- Ray tracing cores
- 142
- L1 cache
- 128 KB
- L2 cache
- 96 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)
- 91.1 TFLOPS
- Single precision (FP32)
- 91.1 TFLOPS
- Double precision (FP64)
- 1.4 TFLOPS
- Pixel rate
- 481 GPixel/s
- Texture rate
- 1,423 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)
- 300 W
- Suggested power supply
- 700 W
- Power connectors
- 1x 16-pin
- Bus interface
- PCIe 4.0 x16
- Slot width
- Dual-slot
- Dimensions
- 267 mm
- Display outputs
- 4x DisplayPort 1.4a
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
- 8.9
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a RTX 6000 Ada Generation
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
Capacity and bandwidth
Memory
48 GB
Bandwidth
960 GB/s
Largest model
Qwen3-Coder-Next
The RTX 6000 Ada Generation carries 48 GB of GDDR6, which covers the mid-sized models most people actually run — about 43.2 GB of it after the runtime and driver reserve their working space.
Bandwidth is 960 GB/s across a 384-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.
The figure is the memory clock — 2.5 GHz here — multiplied by the bus width. It is why core counts predict generation speed so poorly.
The practical ceiling is Qwen3-Coder-Next at 80B, held at IQ4_XS and running at roughly 69.3 tokens per second.
The chip and how it was built
The RTX 6000 Ada Generation is built on the AD102 graphics processor, using NVIDIA's Ada Lovelace architecture, as part of the Workstation Ada(x000A) generation.
The chip is manufactured by TSMC, on a 5 nm process, with a die measuring 609 mm², holding 76.3 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 December 2022, roughly 3 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
91.1 TFLOPS
FP64
1.4 TFLOPS
Tensor cores
568
On paper the RTX 6000 Ada Generation reaches 91.1 TFLOPS at half precision and 91.1 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 1.4 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 568 tensor cores across 142 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 915 MHz at base to 2.51 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 RTX 6000 Ada Generation has 128 KB of L1 cache, backed by 96 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 18,176 shading units, 568 texture mapping units, and 192 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
300 W
The RTX 6000 Ada Generation is rated at 300 W, with a 700 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, measuring 267 mm long, and needs 1x 16-pin. 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 4.0 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 a RTX 6000 Ada Generation can run
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 RTX 6000 Ada Generation
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 RTX 6000 Ada Generation
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
Find the model in the table
The table lists 576 models this RTX 6000 Ada Generation 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
Longer conversations cost memory on top of the weights. With 48 GB to work in, that is frequently the difference between a model fitting and not.
-
03
Pin the comparison to one quality level
By default the table picks the least-compressed copy that fits. Setting a floor removes models that only qualify through heavy compression.
-
04
Read the speed and the range
The figures are calculated, not measured. 407 tok/s on Gemma 3 QAT 1B is the fastest result on this card, and like every row it carries a range that reflects how much the runtime matters.
-
05
Read the fit verdict last
A tight fit runs but leaves no room to raise the context later; comfortable has headroom. The memory column shows what each model needs against the 48 GB available.
-
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 RTX 6000 Ada Generation sits against the alternatives.
Answers
RTX 6000 Ada Generation — common questions
How much cache does a RTX 6000 Ada Generation have?
The RTX 6000 Ada Generation has 128 KB of L1 cache, and 96 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 RTX 6000 Ada Generation?
The RTX 6000 Ada Generation is rated at 91.1 TFLOPS at half precision and 91.1 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 RTX 6000 Ada Generation have?
The RTX 6000 Ada Generation has 568 tensor cores across 142 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 RTX 6000 Ada Generation support CUDA?
Yes. The RTX 6000 Ada Generation reports CUDA compute capability 8.9. 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 RTX 6000 Ada Generation use?
It uses PCIe 4.0 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 RTX 6000 Ada Generation good for running local AI models?
Its memory is large enough for models most desktop hardware cannot touch and its bandwidth is high enough to generate text faster than most people read. In total it runs 576 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a RTX 6000 Ada Generation run a model that does not fit in its memory?
Offloading past the card's 48 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.
Would two RTX 6000 Ada Generation cards be twice as fast?
Pairing RTX 6000 Ada Generation cards buys headroom rather than pace: 96 GB of combined memory, at roughly the same generation speed as one.
What AI models can a RTX 6000 Ada Generation run?
576 of the 679 open-weight language models we track fit on a RTX 6000 Ada Generation 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 RTX 6000 Ada Generation can run?
The largest model in our catalogue that fits on a RTX 6000 Ada Generation is Qwen3-Coder-Next at 80B parameters, compressed to IQ4_XS. It generates roughly 69.3 tokens per second and needs about 38.8 GB of the card's memory.
How many tokens per second does a RTX 6000 Ada Generation produce?
It depends on the model. On a RTX 6000 Ada Generation the fastest model we track is Gemma 3 QAT 1B at about 407 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.
Can a RTX 6000 Ada Generation run a 7B model?
Yes. For example a RTX 6000 Ada Generation runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 60.7 tokens per second.
Can a RTX 6000 Ada Generation run a 13B model?
Yes. For example a RTX 6000 Ada Generation runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 141 tokens per second.
Can a RTX 6000 Ada Generation run a 30B model?
Yes. For example a RTX 6000 Ada Generation runs ERNIE-4.5-VL-28B-A3B at Q8_0, using about 29.2 GB of memory and generating around 80.7 tokens per second.
Can a RTX 6000 Ada Generation run a 70B model?
Yes. For example a RTX 6000 Ada Generation runs Qwen3-Coder-Next at IQ4_XS, using about 38.8 GB of memory and generating around 69.3 tokens per second.
How much memory does a RTX 6000 Ada Generation have?
A RTX 6000 Ada Generation has 48 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 43.2 GB available for a model and its conversation.
What is the memory bandwidth of a RTX 6000 Ada Generation?
The RTX 6000 Ada Generation has 960 GB/s of memory bandwidth, across a 384-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 RTX 6000 Ada Generation use?
It uses GDDR6 clocked at 2.5 GHz. 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 RTX 6000 Ada Generation?
The RTX 6000 Ada Generation is a NVIDIA product, with the chip manufactured by TSMC, on a 5 nm process.
When was the RTX 6000 Ada Generation released?
The RTX 6000 Ada Generation was released in December 2022.
How much power does a RTX 6000 Ada Generation use?
The RTX 6000 Ada Generation has a rated board power of 300 W, and a 700 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.
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.