Calculate the TPS of the FirePro W8000 on local AI models

AMD 4 GB GDDR5 176 GB/s June 2012

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

97 models it can run

679 models in our catalogue altogether

Largest model it holds

DeciLM 6B

5.7B · Q3_K_M · 27.5 tok/s

Fastest model

Gemma 3 QAT 1B

58.1 tok/s · 1B

Which AI models can run on a FirePro W8000?

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.

97 models match

Calculating
Quantisation Fit
58.1 tok/s

35–93 · low confidence

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

35–93 · low confidence

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

35–93 · low confidence

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

35–93 · low confidence

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

35–93 · low confidence

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

35–93 · low confidence

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

32–86 · low confidence

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

32–85 · low confidence

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

32–85 · low confidence

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

32–85 · low confidence

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

32–85 · low confidence

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

29–78 · low confidence

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

29–78 · low confidence

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

29–78 · low confidence

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

29–78 · low confidence

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

28–76 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 54k tokens Q8_0 Comfortable
46.6 tok/s

28–75 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · low confidence

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

27–72 · 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

FirePro W8000 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
4 GB
Memory bandwidth
176 GB/s
Memory type
GDDR5
Memory bus width
256 bit
Memory clock
1.38 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
Tahiti
Architecture
GCN 1.0
Generation
FirePro GCN(Wx000)
Foundry
TSMC
Process size
28 nm
Transistors
4.3 billion
Transistor density
12,300 K/mm²
Die size
352 mm²
Released
14 June 2012

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
900 MHz
Boost clock
900 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
1,792
Texture mapping units
112
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)
3.2 TFLOPS
Double precision (FP64)
806.4 GFLOPS
Pixel rate
29 GPixel/s
Texture rate
101 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)
225 W
Suggested power supply
550 W
Power connectors
2x 6-pin
Bus interface
PCIe 3.0 x16
Slot width
Dual-slot
Dimensions
279 mm
Display outputs
4x DisplayPort 1.2, 1x SDI

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.

DirectX
11.1
OpenGL
4.6
Vulkan
1.2
OpenCL
1.2
Shader model
5.1

Listings

Where to buy a FirePro W8000

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

4 GB

Bandwidth

176 GB/s

Largest model

DeciLM 6B

At 4 GB of GDDR5 the FirePro W8000 is limited to the smaller end of the catalogue. About 3.6 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.

At 176 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.

Bandwidth is clock times bus width, and this card clocks its memory at 1.38 GHz. Both halves matter, and neither is visible in a gaming benchmark.

The practical ceiling is DeciLM 6B at 5.7B, held at Q3_K_M and running at roughly 27.5 tokens per second.

The chip and how it was built

The FirePro W8000 is built on the Tahiti graphics processor, using AMD's GCN 1.0 architecture, as part of the FirePro GCN(Wx000) generation.

The chip is manufactured by TSMC, on a 28 nm process, with a die measuring 352 mm², holding 4.3 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 June 2012, roughly 14 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

806.4 GFLOPS

Double-precision throughput is 806.4 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 900 MHz at base to 900 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 FirePro W8000 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 1,792 shading units, 112 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

225 W

The FirePro W8000 is rated at 225 W, with a 550 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 279 mm long, and needs 2x 6-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 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 FirePro W8000

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 Qwen3.5-4B 4B · Q5_K_M · Feb 2026 26.0 tok/s
  2. 02 Voxtral Mini 4.7B · Q4_K_M · Jul 2025 28.6 tok/s
  3. 03 Typhoon 2.1 Gemma 4B 4B · Q4_K_M · May 2025 33.6 tok/s
  4. 04 Qwen3-4B 4B · Q3_K_M · Apr 2025 39.2 tok/s
  5. 05 Gemma 3 QAT 4B 4B · Q4_K_M · Apr 2025 33.6 tok/s
  6. 06 Gemma 3 4B 4B · Q4_K_M · Mar 2025 33.6 tok/s
  7. 07 Phi-4-Multimodal 5.6B · Q3_K_M · Mar 2025 28.0 tok/s
  8. 08 Minitron 4B 4.2B · Q4_K_M · Nov 2024 32.0 tok/s
  9. 09 XVERSE-MoE-A4.2B 4.2B · Q4_K_M · Apr 2024 32.0 tok/s
  10. 10 DeciLM 6B 5.7B · Q3_K_M · Sep 2023 27.5 tok/s

The fastest AI models on a FirePro W8000

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 58.1 tok/s
  2. 02 Gemma 3 1B 1B · Q8_0 · 1.8 GB 58.1 tok/s
  3. 03 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 58.1 tok/s
  4. 04 OLMo-1B 1B · Q8_0 · 1.8 GB 58.1 tok/s
  5. 05 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 58.1 tok/s
  6. 06 Pythia-1b 1B · Q8_0 · 1.8 GB 58.1 tok/s
  7. 07 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 53.8 tok/s
  8. 08 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 52.9 tok/s
  9. 09 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 52.9 tok/s
  10. 10 DeciCoder-1B 1.1B · Q8_0 · 1.9 GB 52.9 tok/s

Step by step

How to work out the tokens per second of a FirePro W8000

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

    Find the model in the table

    Every one of the 97 models this FirePro W8000 runs is in the table above. Search narrows it by name or by size.

  2. 02

    Decide how long your conversations run

    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 4 GB.

  3. 03

    Set a minimum quality if you need one

    By default the table picks the least-compressed copy that fits. Setting a floor removes models that only qualify through heavy compression.

  4. 04

    Look at the range, not just the number

    The figures are calculated, not measured. 58.1 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 4 GB before settling on one.

  6. 06

    Open the model to compare cards

    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 FirePro W8000 is the right buy for it or merely a card that fits.

Answers

FirePro W8000 — common questions

01

What bus interface does the FirePro W8000 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.

02

Is the FirePro W8000 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 97 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.

03

Can a FirePro W8000 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 4 GB figures on this page assume it.

04

Would two FirePro W8000 cards be twice as fast?

No. A second FirePro W8000 doubles the memory to 8 GB, which lets you hold models neither could hold alone, but generation does not split that way. These figures describe one card.

05

What AI models can a FirePro W8000 run?

97 of the 679 open-weight language models we track fit on a FirePro W8000 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.

06

What is the largest AI model a FirePro W8000 can run?

The largest model in our catalogue that fits on a FirePro W8000 is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 27.5 tokens per second and needs about 3.5 GB of the card's memory.

07

How many tokens per second does a FirePro W8000 produce?

It depends on the model. On a FirePro W8000 the fastest model we track is Gemma 3 QAT 1B at about 58.1 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.

08

How much memory does a FirePro W8000 have?

A FirePro W8000 has 4 GB of GDDR5 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.

09

What is the memory bandwidth of a FirePro W8000?

The FirePro W8000 has 176 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.

10

What type of memory does a FirePro W8000 use?

It uses GDDR5 clocked at 1.38 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.

11

Who makes the FirePro W8000?

The FirePro W8000 is a AMD product, with the chip manufactured by TSMC, on a 28 nm process.

12

When was the FirePro W8000 released?

The FirePro W8000 was released in June 2012.

13

How much power does a FirePro W8000 use?

The FirePro W8000 has a rated board power of 225 W, and a 550 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.

14

How much cache does a FirePro W8000 have?

The FirePro W8000 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.

15

Does the FirePro W8000 support CUDA?

No. CUDA is NVIDIA-only, and the FirePro W8000 is a AMD card. It runs language models through ROCm, Vulkan or Metal depending on the software, which are less mature than the CUDA path — our estimates apply a penalty for that.

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.

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