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
721 models in our catalogue altogether
Largest model it holds
Qwen3-Coder-Next
80B · IQ4_XS · 69.3 tok/s
Fastest model
Gemma 3 QAT 1B
407 tok/s · 1B
Which AI models can run on a RTX 6000 Ada Generation?
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.
607 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 |
LFM2-1.2B ≈ | 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 |
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
RTX 6000 Ada Generation carries 48 GB of GDDR6. That covers the mid-sized models most people actually run. Once the runtime and driver reserve their working space, roughly this much is left: 43.2 GB.
Memory bandwidth reaches 960 GB/s across a bus of 384 bits. 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 bus width multiplied by a memory clock of 2.5 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The practical ceiling is Qwen3-Coder-Next, 80B, compressed to IQ4_XS and generating around 69.3 tokens per second.
The chip and how it was built
RTX 6000 Ada Generation is built on the graphics processor AD102, using the architecture Ada Lovelace from NVIDIA, as part of the generation Workstation Ada(x000A).
The chip is manufactured by TSMC, on a process of 5 nm, 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 3.7796279627424 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 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 reaches 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 a base of 915 MHz to a boost of 2.51 GHz. 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
RTX 6000 Ada Generation has an L1 cache of 128 KB, backed by an L2 cache of 96 MB. 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
RTX 6000 Ada Generation is rated at 300 W, and the suggested system power supply is 700 W. 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 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 that run on a RTX 6000 Ada Generation
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 607 models this card 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. Against 48 GB 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. The fastest result on this card is 407 tok/s on Gemma 3 QAT 1B. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
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 an available 48 GB.
-
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 how it compares against RTX 6000 Ada Generation.
Answers
RTX 6000 Ada Generation — common questions
RTX 6000 Ada Generation— how much cache does it have?
The L1 cache is 128 KB, and the L2 cache is 96 MB. 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.
RTX 6000 Ada Generation— what are its TFLOPS?
It 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.
RTX 6000 Ada Generation— how many tensor cores does it have?
It 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.
RTX 6000 Ada Generation— does it support CUDA?
Yes. It 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.
RTX 6000 Ada Generation— what bus interface does it 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.
RTX 6000 Ada Generation— is it 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 607 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
RTX 6000 Ada Generation— can it 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 them buys headroom rather than pace: 96 GB to work with rather than twice the tokens per second — every figure here is for a single card.
RTX 6000 Ada Generation— which AI models can it run?
607 of the 721 open-weight language models we track fit on this card and can be run locally. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.
RTX 6000 Ada Generation— what is the largest AI model it can run?
The largest model in our catalogue that fits 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.
RTX 6000 Ada Generation— how many tokens per second does it produce?
It depends on the model. The fastest model we track here 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.
RTX 6000 Ada Generation— can it run 7B models?
Yes. For example it runs Gemma 4 E4B at Q8_0, using about 9.8 GB of memory and generating around 90.4 tokens per second.
RTX 6000 Ada Generation— can it run 13B models?
Yes. For example it runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 141 tokens per second.
RTX 6000 Ada Generation— can it run 30B models?
Yes. For example it 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.
RTX 6000 Ada Generation— can it run 70B models?
Yes. For example it runs Qwen3-Coder-Next at IQ4_XS, using about 38.8 GB of memory and generating around 69.3 tokens per second.
RTX 6000 Ada Generation— how much memory does it have?
This card has 48 GB of GDDR6. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 43.2 GB available for a model and its conversation.
RTX 6000 Ada Generation— what is its memory bandwidth?
Memory bandwidth reaches 960 GB/s across a bus of 384 bits. 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.
RTX 6000 Ada Generation— what type of memory does it 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.
RTX 6000 Ada Generation— who makes it?
This is a product of NVIDIA, with the chip manufactured by TSMC, on a process of 5 nm.
RTX 6000 Ada Generation— when was it released?
It was released in December 2022.
RTX 6000 Ada Generation— how much power does it use?
Rated board power is 300 W, and the suggested system power supply is 700 W. 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.