Nvidia + Groq

 

1. The current stack (why it’s constrained)

Components

• HBM (High Bandwidth Memory)
  • Extremely fast, very close to the GPU
  • Expensive, supply-constrained, thermally dense

• CoWoS (TSMC advanced packaging)
  • Required to mount HBM next to GPU
  • Bottleneck: cost, yield, capacity

• CUDA GPU architecture
  • Optimized for training + flexible workloads
  • Inference efficiency is good, but not maximal

Result

✅ Amazing for training
❌ Overkill + expensive for large-scale inference
❌ Memory bandwidth ≠ utilization for token streaming

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2. Groq’s LPU philosophy (why it’s different)

Key ideas

• Deterministic execution
  • No cache misses
  • No dynamic scheduling

• Compiler-defined dataflow
  • Memory access is planned, not guessed

• Inference-only focus
  • Tokens flow like packets through a network

What this avoids

• No HBM requirement

• No CoWoS dependency

• No massive cache hierarchies

👉 Performance comes from predictability, not brute force bandwidth

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3. Where NVLink fits perfectly

NVLink strengths

• Ultra-high bandwidth chip-to-chip interconnect

• Low latency, memory-semantic links

• Scales across:
  • dies
  • packages
  • boards
  • racks

The key insight

If inference is streaming, memory can be distributed.

Instead of:

[GPU + HBM] → [GPU + HBM] → [GPU + HBM]

You get:

[LPU] ⇄ NVLink ⇄ [LPU] ⇄ NVLink ⇄ [LPU]

Each chip:

• Owns a slice of weights

• Streams activations forward

• Never needs full model state locally

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4. The “bypass CoWoS + HBM” architecture

Hypothetical combined stack

┌───────────────┐
│  LPU Die      │  ← Groq-style deterministic core
│  (SRAM-heavy) │
└──────┬────────┘
       │ NVLink
┌──────▼────────┐
│  LPU Die      │
└──────┬────────┘
       │ NVLink
┌──────▼────────┐
│  LPU Die      │
└───────────────┘

Why this works

• Inference = sequential token flow

• Weights = static

• NVLink = model-scale fabric

• SRAM + streaming > HBM + caching

This turns the model into the system, not the chip.

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5. Why this makes sense for Nvidia specifically

Nvidia gains

• Escapes HBM supply + CoWoS bottleneck

• Adds a pure inference SKU

• Keeps NVLink as the moat

• Complements CUDA (training) instead of replacing it

Groq-style IP contributes

• Compiler-driven scheduling

• Deterministic latency (huge for real-time AI)

• Power efficiency at scale

Strategic symmetry

Training	Inference
CUDA GPU	LPU
HBM	Distributed SRAM
CoWoS	NVLink fabric
Throughput	Latency + efficiency

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6. Bottom line

You’re not wrong to say:

“This actually makes a ton of sense.”

Architecturally:

• Training wants flexibility + brute force

• Inference wants determinism + streaming

• NVLink is already the missing glue

• HBM is the wrong tool for token pipelines

Great — let’s make this concrete and visual by walking through a step-by-step token flow in an NVLink-connected, Groq-style inference system, and then contrast it with today’s GPU+HBM approach.

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1. First: the mental picture (what “streaming inference” really means)

Key property of LLM inference

• Tokens are generated one at a time

• Each token:

  1. Passes through every layer

  2. Uses fixed weights

  3. Produces one output vector

This is not a big parallel matrix problem like training.
It’s a pipeline problem.

---

2. Traditional GPU inference (why HBM is overkill)

What happens today (simplified)

For every token:

1. Load weights from HBM

2. Move activations through caches

3. Synchronize warps

4. Stall on cache misses

5. Repeat for next layer

Consequences

• 🔥 Massive bandwidth use

• ⏳ Unpredictable latency

• 💸 Expensive silicon (HBM + CoWoS)

• 😬 GPU is solving problems inference doesn’t have

GPUs assume:

“You might need anything, anytime.”

Inference reality:

“I know exactly what I need and when.”

---

3. Groq-style LPU token flow (deterministic pipeline)

Step-by-step (single chip)

1. Token embedding enters

2. Layer 1 compute starts immediately

3. Output streams directly into Layer 2

4. No cache lookup

5. No dynamic scheduling

6. No stalls

Think of it like:

• An assembly line

• Not a warehouse with forklifts

Latency becomes:

latency ≈ number_of_layers × fixed_cycle_cost

Predictable. Boring. Fast.

---

4. Now add NVLink: model-scale pipelining

Split the model across chips

Example: 96-layer model, 6 chips

Chip	Layers
LPU 1	1–16
LPU 2	17–32
LPU 3	33–48
LPU 4	49–64
LPU 5	65–80
LPU 6	81–96

Token journey

Token t
 ↓
[LPU 1] → NVLink → [LPU 2] → NVLink → ... → [LPU 6]
 ↓
Output token

While:

• LPU 2 processes token t

• LPU 1 already processes token t+1

➡️ Full pipeline utilization

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5. Why NVLink is the secret weapon

NVLink advantages here

• Memory-semantic (not packet guessing)

• Deterministic latency

• Orders of magnitude faster than PCIe

• Designed for tight synchronization

This lets Nvidia:

• Treat multiple chips as one logical inference engine

• Without HBM coherence nightmares

• Without giant monolithic dies

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6. SRAM + streaming beats HBM + caching (for inference)

Why SRAM is enough

• Weights are:

  • Static

  • Reused

  • Predictable

• Access patterns are known at compile time

So:

• Keep hot weights in SRAM

• Stream cold weights once

• Never “hunt” for data

HBM is amazing when:

• Access is random

• Working set is huge

• Compute is chaotic

Inference is none of those.

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7. Why this scares everyone (and excites Nvidia)

Hyperscalers

• Lose differentiation if inference becomes “plug-and-play”

• GPU margins shrink at scale

Nvidia upside

• Owns:

  • NVLink

  • Compiler stack

  • Ecosystem

• Can sell:

  • GPUs for training

  • LPUs for inference

  • One fabric for both

Strategic punchline

Training creates intelligence.
Inference delivers intelligence.
Nvidia already owns the road between them.

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8. One-sentence takeaway

Inference wants to be a factory line, not a warehouse — and NVLink turns multiple small, deterministic chips into one massive, perfectly synchronized machine.

If you want next:

• 🔍 Where this breaks (very large context windows)

• ⚖️ Comparison vs TPU / Cerebras

• 🔧 What compiler magic is required

• 📈 Why latency SLAs change everything

Just tell me which direction to go.

which hyperscalers and what can they do

Here’s a clear, non-hand-wavy map of which hyperscalers are most exposed to an Nvidia-style deterministic inference fabric (GPU for training, LPU-like for inference), why, and what realistic moves they have.

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The hyperscalers (ranked by exposure)

1. Google (DeepMind + GCP) — most exposed

Why

• TPU already is a deterministic, inference-friendly architecture

• Heavy internal reliance on TPU v4/v5 for cost control

• NVLink-style external fabric threatens TPU’s differentiation

What Google can do

• ✅ Double down on TPU

  • Push TPU as the inference + training stack

  • Keep it vertically integrated (hardware → compiler → model)

• ⚠️ Downside:

  • TPU is not an ecosystem

  • Customers want portability, not lock-in

Reality

Google is the only hyperscaler already playing this game — and Nvidia is trying to take it from them.

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2. AWS (Amazon) — economically exposed, strategically flexible

Why

• Largest inference bill by far

• Massive deployment of Inferentia / Trainium

• Margins matter more to AWS than prestige silicon

What AWS can do

• ✅ Scale Inferentia hard

  • Improve compiler + model support

  • Compete on price-per-token

• ✅ Play vendors against each other

  • Nvidia for premium

  • Inferentia for volume

• ❌ Cannot abandon Nvidia entirely (customer demand)

Reality

AWS doesn’t need to win architecturally — it just needs inference to be cheap.

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3. Microsoft Azure — strategically trapped

Why

• OpenAI dependency

• Public commitment to Nvidia

• Less internal silicon maturity for inference

What Microsoft can do

• ⚠️ Incremental custom accelerators

  • Late to the party

• ⚠️ Lean on Nvidia partnerships

• ❌ Very hard to pivot without breaking OpenAI alignment

Reality

Microsoft is the most locked-in hyperscaler right now.

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4. Meta (Facebook) — dangerous wildcard

Why

• Massive inference workloads (feeds, ads, assistants)

• Strong internal hardware teams

• Open-source models = inference at scale

What Meta can do

• ✅ Design internal LPUs

  • Meta already builds custom inference silicon

• ✅ Optimize models for streaming inference

• ✅ Undercut everyone on cost

• ❌ Doesn’t sell cloud → no ecosystem leverage

Reality

Meta doesn’t need customers — just efficiency. That makes them dangerous.

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5. Apple (honorable mention)

Why

• Edge inference, not cloud

• Already uses deterministic accelerators (Neural Engine)

Reality

Apple is proof this architecture works — just not a cloud competitor.

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What hyperscalers can actually do (options matrix)

Option	Who can pull it off	Risk
Build in-house LPUs	Google, Meta, AWS	Compiler + ecosystem burden
Price war on inference	AWS	Margin compression
Lock customers in	Google	Customer backlash
Partner deeply with Nvidia	Microsoft	Vendor dependence
Open inference standards	Meta (maybe)	Helps competitors too

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Why Nvidia’s move is uniquely threatening

Hyperscalers can build chips.
They cannot easily build ecosystems.

Nvidia already has:

• NVLink (fabric)

• CUDA / compiler stack

• Deployment mindshare

• Model vendor alignment

If Nvidia adds:

• Deterministic inference silicon

• That bypasses HBM and CoWoS

• While plugging into the same software stack

Then hyperscalers face a brutal choice:

Build everything themselves — or buy a cheaper, better, integrated solution.

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The uncomfortable truth for hyperscalers

• Training is prestige

• Inference is cost

• Whoever wins inference wins AI margins

And inference is moving toward:

• Deterministic

• Streaming

• Fabric-scaled

• Boring (in the best way)

That’s exactly Nvidia’s comfort zone.

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One-line takeaway

Hyperscalers can build chips, but Nvidia is building the road system — and inference traffic is about to explode.