Calculate the TPS of the Quadro K5100M on local AI models

NVIDIA 8 GB GDDR5 115 GB/s July 2013

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

337 models it can run

679 models in our catalogue altogether

Largest model it holds

Baichuan 1-13B

13.3B · Q3_K_M · 8.4 tok/s

Fastest model

Gemma 3 QAT 1B

41.5 tok/s · 1B

Which AI models can run on a Quadro K5100M?

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.

337 models match

Calculating
Quantisation Fit
41.5 tok/s

15–83 · low confidence

Gemma 3 1B 1B Mar 2025 1.8 GB 33k tokens Q8_0 Comfortable
41.5 tok/s

15–83 · low confidence

Gemma 3 QAT 1B 1B Apr 2025 1.8 GB 33k tokens Q8_0 Comfortable
41.5 tok/s

15–83 · low confidence

HGRN 1B (WT 103) ≈ 1B Nov 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
41.5 tok/s

15–83 · low confidence

LLama 3..2 Typhoon 2 1B ≈ 1B Dec 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
41.5 tok/s

15–83 · low confidence

OLMo-1B ≈ 1B Feb 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
41.5 tok/s

15–83 · low confidence

Pythia-1b ≈ 1B Apr 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
38.4 tok/s

13–77 · low confidence

OpenELM-1.1B ≈ 1.1B May 2024 1.9 GB 131k tokens ? Q8_0 Comfortable
37.7 tok/s

13–75 · low confidence

DeciCoder-1B ≈ 1.1B Aug 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
37.7 tok/s

13–75 · low confidence

SantaCoder ≈ 1.1B Jan 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
37.7 tok/s

13–75 · low confidence

TinyLlama-1.1B (1T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
37.7 tok/s

13–75 · low confidence

TinyLlama-1.1B (3T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
34.6 tok/s

12–69 · low confidence

EXAONE 4.0 (1.2B) ≈ 1.2B Jul 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
34.6 tok/s

12–69 · low confidence

MinerU2.5 ≈ 1.2B Sep 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
34.6 tok/s

12–69 · low confidence

Pleias 1.0 1.2B ≈ 1.2B Dec 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
34.6 tok/s

12–69 · low confidence

Pleias-RAG-1B ≈ 1.2B Apr 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
33.7 tok/s

12–67 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 131k tokens Q8_0 Comfortable
33.3 tok/s

12–66 · low confidence

MiniCPM-1.2B ≈ 1.2B Jun 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

DeepSeek Coder 1.3B ≈ 1.3B Jan 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

DeepSeek-VL-1.3B ≈ 1.3B Mar 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

DigiRL ≈ 1.3B Jun 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

GLA Transformer 1.3B ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

Janus 1.3B ≈ 1.3B Oct 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

Kosmos-2.5 ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · low confidence

Otter ≈ 1.3B May 2023 2.1 GB 131k tokens ? Q8_0 Comfortable
31.9 tok/s

11–64 · 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

Quadro K5100M 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
8 GB
Memory bandwidth
115 GB/s
Memory type
GDDR5
Memory bus width
256 bit
Memory clock
900 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
GK104
Architecture
Kepler
Generation
Quadro Kepler-M(Kx100M)
Foundry
TSMC
Process size
28 nm
Transistors
3.5 billion
Transistor density
12,000 K/mm²
Die size
294 mm²
Package
BGA-1745
Released
23 July 2013

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
771 MHz
Boost clock
771 MHz

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
1,536
Texture mapping units
128
Render output units
32
L1 cache
16 KB
L2 cache
0.5 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.

Single precision (FP32)
2.4 TFLOPS
Double precision (FP64)
98.7 GFLOPS
Pixel rate
25 GPixel/s
Texture rate
99 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)
100 W
Power connectors
None
Bus interface
MXM-B (3.0)
Slot width
MXM Module

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
3.0
DirectX
11.0
OpenGL
4.6
Vulkan
1.2
OpenCL
3.0
Shader model
5.1

Listings

Where to buy a Quadro K5100M

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

What the memory subsystem means for AI

Memory

8 GB

Bandwidth

115 GB/s

Largest model

Baichuan 1-13B

At 8 GB of GDDR5 the Quadro K5100M is limited to the smaller end of the catalogue. About 7.2 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.

At 115 GB/s across a 256-bit bus, bandwidth is this card's real constraint. Every token requires reading the entire model out of memory, so a large model will feel slow here even when it fits.

That comes from a 900 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.

In practice that combination tops out at Baichuan 1-13B — 13.3B, compressed to Q3_K_M, generating around 8.4 tokens per second.

The chip and how it was built

The Quadro K5100M is built on the GK104 graphics processor, using NVIDIA's Kepler architecture, as part of the Quadro Kepler-M(Kx100M) generation.

The chip is manufactured by TSMC, on a 28 nm process, with a die measuring 294 mm², holding 3.5 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 July 2013, roughly 13 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

FP64

98.7 GFLOPS

Double-precision throughput is 98.7 GFLOPS. 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.

Clocks run from 771 MHz at base to 771 MHz 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 Quadro K5100M has 16 KB of L1 cache, backed by 0.5 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 1,536 shading units, 128 texture mapping units, and 32 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

100 W

The Quadro K5100M is rated at 100 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 a mxm module. 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 MXM-B (3.0). 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 Quadro K5100M

The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.

  1. 01 OLMo 2 Furious 13B 13B · Q3_K_M · Dec 2024 8.6 tok/s
  2. 02 Cambrian-1-13B 13B · Q3_K_M · Jun 2024 8.6 tok/s
  3. 03 Fugaku-LLM 13B · Q3_K_M · May 2024 8.6 tok/s
  4. 04 OpenThaiGPT v1.0.0 (13B) 13.1B · Q3_K_M · Apr 2024 8.5 tok/s
  5. 05 Aya 13B · Q3_K_M · Feb 2024 8.6 tok/s
  6. 06 Elyza 13B · Q3_K_M · Dec 2023 8.6 tok/s
  7. 07 NexusRaven-V2 13B · Q3_K_M · Dec 2023 8.6 tok/s
  8. 08 Baize-v2-13B (白泽) 13B · Q3_K_M · Dec 2023 8.6 tok/s
  9. 09 Stockmark-13B 13.2B · Q3_K_M · Oct 2023 8.5 tok/s
  10. 10 Baichuan 1-13B 13.3B · Q3_K_M · Jul 2023 8.4 tok/s

The fastest AI models on a Quadro K5100M

Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.

  1. 01 Gemma 3 QAT 1B 1B · Q8_0 · 1.8 GB 41.5 tok/s
  2. 02 Gemma 3 1B 1B · Q8_0 · 1.8 GB 41.5 tok/s
  3. 03 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 41.5 tok/s
  4. 04 OLMo-1B 1B · Q8_0 · 1.8 GB 41.5 tok/s
  5. 05 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 41.5 tok/s
  6. 06 Pythia-1b 1B · Q8_0 · 1.8 GB 41.5 tok/s
  7. 07 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 38.4 tok/s
  8. 08 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 37.7 tok/s
  9. 09 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 37.7 tok/s
  10. 10 DeciCoder-1B 1.1B · Q8_0 · 1.9 GB 37.7 tok/s

Step by step

How to work out the tokens per second of a Quadro K5100M

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.

  1. 01

    Start with the model, not the specification

    All 337 models the Quadro K5100M handles are already listed. The search box takes a name or a size such as 27b, which matches on parameter count.

  2. 02

    Set the context length you will actually use

    Set the context to your real working length. Short questions cost almost nothing; a long document can consume a large share of the card's 8 GB.

  3. 03

    Set a minimum quality if you need one

    Each model is shown at the best compression this card can hold. A minimum quality hides the ones that only fit by being squeezed further than you would accept.

  4. 04

    Take the range as the answer

    The figures are calculated, not measured. 41.5 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.

  5. 05

    Read the fit verdict last

    The fit column separates models that just fit from those with room to spare — worth checking against the card's 8 GB before settling on one.

  6. 06

    Check the same model from the other side

    Every model name in the table links to its own page, which runs the same calculation across every card we hold. That is where you see whether the Quadro K5100M is the right buy for it or merely a card that fits.

Answers

Quadro K5100M — common questions

01

Who makes the Quadro K5100M?

The Quadro K5100M is a NVIDIA product, with the chip manufactured by TSMC, on a 28 nm process.

02

When was the Quadro K5100M released?

The Quadro K5100M was released in July 2013.

03

How much power does a Quadro K5100M use?

The Quadro K5100M has a rated board power of 100 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.

04

How much cache does a Quadro K5100M have?

The Quadro K5100M has 16 KB of L1 cache, and 0.5 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.

05

Does the Quadro K5100M support CUDA?

Yes. The Quadro K5100M reports CUDA compute capability 3.0, which predates tensor cores. 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.

06

What bus interface does the Quadro K5100M use?

It uses MXM-B (3.0). 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.

07

Is the Quadro K5100M good for running local AI models?

Its memory limits it to smaller models though its bandwidth means generation will feel slow on larger models. In total it runs 337 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.

08

Can a Quadro K5100M run a model that does not fit in its memory?

It can be split, with the overflow held in system memory — but that part drags the whole thing down, and none of the 8 GB figures on this page assume it.

09

Would two Quadro K5100M cards be twice as fast?

Capacity adds, throughput does not. Two of them give you 16 GB to work with rather than twice the tokens per second — every figure here is for a single Quadro K5100M.

10

What AI models can a Quadro K5100M run?

337 of the 679 open-weight language models we track fit on a Quadro K5100M 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.

11

What is the largest AI model a Quadro K5100M can run?

The largest model in our catalogue that fits on a Quadro K5100M is Baichuan 1-13B at 13.3B parameters, compressed to Q3_K_M. It generates roughly 8.4 tokens per second and needs about 7.2 GB of the card's memory.

12

How many tokens per second does a Quadro K5100M produce?

It depends on the model. On a Quadro K5100M the fastest model we track is Gemma 3 QAT 1B at about 41.5 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.

13

Can a Quadro K5100M run a 7B model?

Yes. For example a Quadro K5100M runs MetaMath 7B (LLaMa finetune) at Q4_K_M, using about 6.5 GB of memory and generating around 13.7 tokens per second.

14

Can a Quadro K5100M run a 13B model?

Yes. For example a Quadro K5100M runs Gemma 4 12B at Q3_K_M, using about 6.5 GB of memory and generating around 9.4 tokens per second.

15

How much memory does a Quadro K5100M have?

A Quadro K5100M has 8 GB of GDDR5 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 7.2 GB available for a model and its conversation.

16

What is the memory bandwidth of a Quadro K5100M?

The Quadro K5100M has 115 GB/s of memory bandwidth, across a 256-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.

17

What type of memory does a Quadro K5100M use?

It uses GDDR5 clocked at 900 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.

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.

All GPUs