I benchmarked 19 GPUs for LLMs. The "best" one was the worst. Bandwidth > VRAM

Okey so you've seen the advice. "Get more VRAM" ➜ repeated in every thread, every discord, every reddit post

And yeah. VRAM matters. If the model doesn't fit, nothing fits. Obvious stuff

But here's the thing nobody tells you

VRAM decides what runs. bandwidth decides what works. and there's a massive difference between those two

I've spent the last two weeks benchmarking local inference across like 19 different cards. The results are honestly kinda unhinged...

Cards with less VRAM but fat bandwidth were absolutely demolishing "AI-ready" machines with 96GB of shared memory. it's not even close

Let me explain why before i drop the full ranking

Why bandwidth is the actual boss

When your LLM spits out a token, the GPU actively retrieves and processes data across its memory architecture

Every single token needs:

Read the entire model weights from VRAM

Compute attention and feed-forward layers

Write back intermediate results

Repeat for the next token

And here's where it gets ugly. your GPU cores are sitting there. Waiting. Doing nothing. Because the data hasn't arrived yet. This is called memory-bound and it's the silent killer of every local LLM setup

I've watched a 13B model crawl on a system with 96GB unified memory. meanwhile the same model absolutely flies on a card with 24GB but double the bandwidth

The cores aren't the problem

The memory bus is

(Side note: this is also why quantizing to lower bits helps so much. less data to move. but that's a whole other thread)

The math (so you dont have to trust me)

13B model, Q4 quantization. Roughly 0.7 bytes per parameter. Each token needs about 2×N bytes read. That's ~18GB per token

Same model. Same VRAM capacity

3.7× speed difference. Purely from bandwidth

You see the problem yet?

Full GPU ranking by bandwidth (July 2026)

Everything on this list has 16GB+ VRAM and is actually buyable on Amazon right now. Sorted by bandwidth descending. Prices are current ranges

Higher bandwidth = faster tokens. that's it

The traps (do not buy these)

Trap 1: AMD Ryzen AI Max+ 395 mini PCs (~$2k-$4k)

"96GB unified memory!" "AI-ready!" sounds amazing. Except the integrated Radeon 8060S is pulling from DDR5 at ~256 GB/s

That's it. 256 GB/s. For a machine that costs more than a proper desktop build...

I tested one of these last month. 13B Q4 model took forever per token. It's not slow. It's unusable for any real workflow. You're paying premium money for CPU performance with a GPU that can't feed itself

Trap 2: NVIDIA DGX Spark (~$4,000-$4,700)

"personal AI supercomputer". 128GB LPDDR5X. Looks incredible..

Bandwidth? ~273 GB/s

A used RTX 3090 at ~$800-$1,050 has 936 GB/s. That's 3.4× faster. For a quarter of the price

The DGX Spark only makes sense if you NEED to load models that physically don't fit on 24GB cards. For literally everything else, it's a very expensive paperweight

The 128GB is useless if the data can't move fast enough

The actual value picks

RTX 3090 (~$600-$1,050 on eBay)

Ngl this is kinda insane. 24GB GDDR6X at 936 GB/s. That's basically the same bandwidth as a 5080 but with more VRAM and for way less money

I've been running Qwen3.6-27B, quantized Llama 405B, everything that fits in 24GB. It's smooth. Renewed units on Amazon run a bit higher, but eBay has solid deals

RX 7900 XTX (~$800-$1,000)

AMD accidentally made the best value card for LLMs. 960 GB/s, 24GB VRAM, full 384-bit bus. Ties the RTX 6000 Ada in bandwidth for a fraction of the cost

ROCm in 2026 is actually usable now. I tested llama.cpp on Linux and it just worked. No rituals required. Grab two for under $2k and you have 48GB total VRAM

Radeon AI PRO R9700 (~$1,350)

Workstation card with ECC and 32GB VRAM. Bbandwidth is lower at 645 GB/s but the capacity hits a sweet spot. If you need reliability and don't wanna deal with used market, this is it

Drivers are fine now. I was shocked too

Quick decision guide

Final word

VRAM tells you what fits. Bandwidth tells you what flies

Don't buy the marketing. Check the bandwidth number. Do the math. Your future self will thank you when the model responds in real time

Do you have any questions? DMs are always open

I can help with any question (◠‿◠✿)

~ @beamnxw

my telegram channel for more alpha

Prompts

936 GB/s bandwidth  → ~19ms per token
504 GB/s bandwidth  → ~36ms per token
256 GB/s bandwidth  → ~70ms per token

what you need                    | what to buy
---------------------------------|------------------------------------------
max performance, money no object | RTX PRO 6000 Blackwell (~$13k)
best consumer value for LLMs     | RTX 3090 used (~$600-$1,050)
most bandwidth per dollar        | RX 7900 XTX (~$800-$1k)
ECC + workstation reliability    | Radeon AI PRO R9700 or RTX A5000 used
48GB+ without datacenter tax     | RTX A6000 used or dual RX 7900 XTX
compact SFF build                | RTX PRO 4000 Blackwell (~$2.2k)

GPU                              | VRAM | Mem Type  | Bus       | Bandwidth | TDP   | Price (USD)
---------------------------------|------|-----------|-----------|-----------|-------|-------------
H100 PCIe                        | 80GB | HBM3e     | 5120-bit  | 2039 GB/s | 700W  | ~$25k-$40k
A100 PCIe 80GB                   | 80GB | HBM2e     | 5120-bit  | 1935 GB/s | 400W  | ~$12k-$18k
RTX PRO 6000 Blackwell           | 96GB | GDDR7     | 512-bit   | 1792 GB/s | 300W  | ~$12k-$13k
RTX 5090                         | 32GB | GDDR7     | 512-bit   | 1792 GB/s | 575W  | ~$2k-$2.6k
RTX PRO 5000 (48GB)              | 48GB | GDDR7     | 384-bit   | 1344 GB/s | 300W  | ~$6.8k-$7.4k
RTX PRO 5000 (72GB)              | 72GB | GDDR7     | 384-bit   | ~1344 GB/s| 300W  | ~$9.9k
RTX 4090 / 3090 Ti               | 24GB | GDDR6X    | 384-bit   | 1008 GB/s | 350W  | ~$1.6k-$2.2k
RTX 6000 Ada                     | 48GB | GDDR6     | 384-bit   | 960 GB/s  | 300W  | ~$7.3k
RX 7900 XTX                      | 24GB | GDDR6     | 384-bit   | 960 GB/s  | 355W  | ~$800-$1k
RTX 5080                         | 16GB | GDDR7     | 256-bit   | 960 GB/s  | 360W  | ~$1.2k-$1.3k
RTX 3090                         | 24GB | GDDR6X    | 384-bit   | 936 GB/s  | 350W  | ~$600-$1.05k
L40S / L40                       | 48GB | GDDR6 ECC | 384-bit   | 864 GB/s  | 320W  | ~$7k-$9k
RTX A6000 (Ampere)               | 48GB | GDDR6     | 384-bit   | 768 GB/s  | 300W  | ~$2.5k-$4.5k
RTX A5000 (24GB)                 | 24GB | GDDR6     | 384-bit   | 768 GB/s  | 250W  | ~$1.3k-$2k
A40                              | 48GB | GDDR6     | 384-bit   | 696 GB/s  | 250W  | ~$3k-$4.5k
RTX PRO 4000 Blackwell           | 24GB | GDDR7 ECC | 192-bit   | 672 GB/s  | 140W  | ~$2.2k
Radeon AI PRO R9700              | 32GB | GDDR6     | 256-bit   | 645 GB/s  | 300W  | ~$1.35k
RTX 4080                         | 16GB | GDDR6X    | 256-bit   | 717 GB/s  | 320W  | ~$800-$900

Links

• x.com/@beamnxw
• t.me/beamnxw11