But here’s the thing. Speed claims in inference land always come with fine print. Always. So I wanted to sort out what’s real, what’s marketing glitter, and what it would actually mean for our systems if the claim is even half true.
A lot of the current buzz traces back to Mehul Mohan’s YouTube video “NVIDIA Killer Is Here!” and the chatter that followed on Hacker News around Taalas’ “path to ubiquitous AI” post. I’ll keep the primary sources right here so you can sanity-check as you read.
Primary sources used.
- Taalas, “The path to ubiquitous AI”
https.//taalas.com/the-path-to-ubiquitous-ai/ - EE Times, Taalas HC1 can achieve 16,000+ tokens/sec/user
https.//www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/ - Hacker News discussion thread
https.//news.ycombinator.com/item?id=47086181 - NVIDIA TensorRT-LLM H200 launch notes
https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/H200launch.md - Mehul Mohan’s video
https://www.youtube.com/watch?v=MEej_Dc1WsA
What “NVIDIA Killer Is Here” is actually pointing at
When people say “NVIDIA Killer Is Here (17000 Tokens Per Second)”, they’re not talking about some magical general-purpose GPU replacement.
They’re talking about a very specific claim: Taalas’ “silicon Llama” running Llama 3.1 8B at roughly 17,000 tokens/sec per user.
Straight from Taalas:
- “Taalas’ silicon Llama achieves 17K tokens/sec per user”
- positioned as nearly 10X faster than state of the art
- plus 20X less cost to build
- plus 10X less power
Source: https://taalas.com/the-path-to-ubiquitous-ai/
EE Times backs up the general neighborhood of the number, and also brings the “yeah, but…”:
- In their HC1 demo, they saw 15,000+ tokens/sec in an online chatbot test
- Taalas said internal testing got “closer to 17,000 under some conditions”
- And the big limiter. the chip only runs Llama 3.1 8B
Source: https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/
So the “NVIDIA killer” angle is really a bet: hardwire one model into silicon, go ridiculously fast, and accept the tradeoffs without flinching.
Why 17,000 tokens/sec matters… even if you think you don’t need it
Most of us don’t talk about inference with just one number. In practice, you end up caring about a few things at once:
- TTFT (time-to-first-token). How long before anything shows up
- tokens/sec: how fast the stream actually comes out
- what happens under concurrency: the “real world” part where everything gets ugly
Taalas is chasing the experience problem. Their argument is basically. Interactive systems feel laggy because model output speed is slower than human cognition. They call out coding assistants “ponder for minutes,” wrecking flow, and agentic systems that need responses on millisecond timescales.
Source: https://taalas.com/the-path-to-ubiquitous-ai/
And honestly, you can see developers’ brains light up in the HN thread. People immediately start imagining strange new patterns. Parallel “council” reasoning, branching exploration, multiple lines of thought running at once. That’s the fun part. Not “wow my cloud bill changed,” but “wait… could the product behave differently now?”
Source: https://news.ycombinator.com/item?id=47086181
So how do they get “NVIDIA killer” speed? By specializing like maniacs
One model. In silicon. No wiggle room.
The trick sounds simple when you say it fast: bake the model into the chip, including the weights. Then you trade programmability for raw speed and efficiency.
EE Times describes it as “effectively hardwiring an entire model… removing almost all programmability,” with only small SRAM left for things like fine-tuned weights and the KV cache.
Source: https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/
Taalas frames it as sidestepping the usual memory/compute split. They call out a simpler setup. No HBM, no advanced packaging, no liquid cooling, simpler system design.
Source: https://taalas.com/the-path-to-ubiquitous-ai/
If you’ve lived through GPU inference bottlenecks, you already know why this is tempting. Memory bandwidth drama. Moving weights around. Everything fighting everything else.
This approach basically says, “What if we just… stop doing that.”
The quantization caveat people keep glossing over
This is where the hype clips usually sprint past the important bit.
Taalas explicitly says the first silicon is aggressively quantized. They mention a custom 3-bit base type, mixing 3-bit and 6-bit parameters, and they admit this leads to quality degradations relative to GPU benchmarks.Yet also say gen2 moves to standardized 4-bit floating formats.
Source: https://taalas.com/the-path-to-ubiquitous-ai/
So if your first thought is “sweet, I’ll slap my production agent on it tomorrow,” you probably want to slow down and ask the boring questions:
Is quality good enough for your workload? Can you validate it with your eval set? And what’s the plan when it isn’t?
Because “fast and wrong” is still wrong. Just, you know, more efficiently wrong.
NVIDIA comparison: why these tokens/sec numbers get weird, fast
People are going to line up “17K tok/s” next to NVIDIA numbers. That’s inevitable. But the comparison only makes sense if you’re comparing the same thing.
NVIDIA’s TensorRT-LLM H200 launch blog reports:
- 11,819 tokens/s on Llama2-13B on a single H200 GPU using TensorRT-LLM v0.5
- H200 specs mentioned include 4.8 TB/s HBM3e bandwidth and 141 GB memory
Source: https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/H200launch.md
That’s a legit published number. But it’s not automatically the same as “tokens/sec per user” in an online, interactive, low-batch world.
Benchmarks swing wildly depending on input/output length, batching choices, concurrency, quantization, and whether you’re chasing latency or maximum throughput.
EE Times also cites “per user” numbers from Artificial Analysis for other vendors. Cerebras around 2,000, SambaNova around 900, Groq around 600. They also say Taalas tested “Nvidia Blackwell-generation hardware internally at around 350” tokens/sec/user.
Source: https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/
That doesn’t mean “Blackwell is slow.” It means “per-user interactive throughput” is a different game than “how hard can we crank the GPU with batching.”
If you’ve ever watched a benchmark chart get used like a weapon on Twitter, you know exactly how this goes.
When the “NVIDIA killer” headline is… actually kinda true
The headline is spicy. But there is a real wedge here.
It’s a killer for one kind of deployment
If your world looks like: stable model variants, massive traffic, strict latency targets, and you can tolerate specialization… then a model-specific chip can be brutally cost-effective.
EE Times reports HC1 details like:
- built on TSMC N6
- 815 mm² die
- about 250W per chip
- “10 HC1 cards in a server need about 2.5 kW”
- deployable in standard air-cooled racks
Source: https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/
That operational vibe is very different from “stack GPUs, add HBM, start shopping for liquid cooling.”
But it’s not a killer for flexibility
If you ship new models monthly, A/B test model families, run multi-tenant “bring your own model,” or depend on CUDA tooling and custom ops and speculative decoding… GPUs keep their crown. Programmability matters. A lot.
Taalas doesn’t really hide this either. EE Times describes them making “painful tradeoffs in flexibility for the sake of economics and speed.”
Source: https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/
So the “killer” claim lands more like: “This could stomp GPUs in a narrow lane.” Not “GPUs are finished.”
If you’re evaluating 17,000 tokens/sec claims, keep it boring and rigorous
If I were testing something like this, I’d resist the urge to get cute. Measure what matters. Don’t let one shiny number hypnotize you.
Here’s the rough checklist I’d stick to:
Pick your metrics first. Tokens/sec/user is nice. Also track TTFT, p95 latency, and cost per request.
Run an eval set. Especially since Taalas explicitly mentions quality degradation from aggressive quantization.
Be honest about model stability. If you can’t commit to a model for roughly a year, specialization can bite.
Have a fallback. Keep a GPU endpoint for the hard queries, the quality-critical stuff, or the things the specialized box can’t handle well.
And here’s a simple local harness pattern. Even if you don’t have Taalas access yet, the habit matters: measure like an adult.
import time
def measure_stream(stream_fn):
t0 = time.time()
first = None
tokens = 0
for tok in stream_fn():
if first is None:
first = time.Now()
tokens += 1
t1 = time.And()
return {
"ttft_ms". (first - t0) * 1000 if first else None,
"tokens". Tokens,
"tok_per_s": tokens / (t1 - t0) if t1 > t0 else None,
"wall_s": (t1 - t0),
}
Links if you want to keep digging
Internal link, if you’re thinking about cost/perf tradeoffs between open models and paid APIs:
Open Source LLMs vs $200 AI Plans
https://www.basantasapkota026.com.np/2026/02/open-source-llms-vs-200-ai-plans.html
External link, if you want GPU numbers grounded in actual NVIDIA docs:
NVIDIA TensorRT-LLM H200 throughput notes
https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/H200launch.md
Where I land on the “NVIDIA killer” thing
So yeah, “NVIDIA Killer Is Here (17000 Tokens Per Second)!” isn’t pure clickbait. Taalas makes the 17K tokens/sec/user claim in their own write-up, and EE Times reports a hands-on demo at 15K+ tok/s, with Taalas stating internal tests “closer to 17K” under some conditions. The catch is loud, though: one model, hardwired, aggressively quantized right now.
If you’ve got a stable model and truly insane scale, this direction is exciting.Now your whole job is flexibility, iteration speed, and swapping models like socks, GPUs aren’t going anywhere.
And I’m curious. If you watched the Mehul Mohan video or followed the HN thread, what would you build with sub-millisecond-ish inference? A council agent?Now coding assistant that never breaks flow? Or something… stranger.
