AI Response Comparison • 8 providers • 78% agreement • 25% divergence
chatgpt
You’re already getting very solid performance out of that P4 with Qwen3-4B, so you’re clearly doing things right. Let’s walk through what’s realistic for:
- **Llama 3.3 70B Instruct**
- **GPT-OSS 20B**
on your **Tesla P4 (8 GB VRAM)** + **i7-8700 / 32 GB RAM**.
I’ll assume:
- You’re using something like **llama.cpp / koboldcpp / text-generation-webui** with **Q4\_K\_M** or similar.
- You’re okay with **CPU offload** when VRAM is insufficient.
- You’re comparing **tokens per second (tps)** at similar context (e.g., 8k) and similar quantization.
---
## 1. Baseline: Your Current Setup
You reported:
- **Model**: Qwen3-4B
- **Quantization**: Q4\_K\_M
- **Context**: 8192
- **Speed**: ~25–30 tps
- **All layers on GPU**
That’s very consistent with what a Tesla P4 can do for a small 4B model:
- P4 is roughly in the **5–6 TFLOPS FP16** class (depending on exact workload).
- 4B models in Q4 can fit fully in 8 GB VRAM, so:
- **No CPU offload**
- **No PCIe bottleneck**
- **Good cache locality**
- Result: 20–40 tps is typical for 4B on this kind of GPU.
This is your “best case” scenario: small model, fully GPU-resident.
---
## 2. Llama 3.3 70B Instruct on a Tesla P4
### 2.1. Can it even fit?
A 70B model, even heavily quantized, is **far beyond 8 GB VRAM**.
Approximate memory footprint (very rough, but good enough):
- 70B parameters
- Q4 quantization ≈ **4 bits/param = 0.5 bytes/param**
- 70B × 0.5 bytes ≈ **35 GB** just for weights
- Plus KV cache, activations, over…
qwen
You're running a very capable workstation with the **Lenovo P330 Tiny**, equipped with an **Intel Core i7-8700 (6-core/12-thread)**, **32GB of RAM**, a **1TB NVMe SSD**, and crucially — an **NVIDIA Tesla P4** with **8GB VRAM**. You're using **Windows 10 LTSC**, and you're currently running **Qwen3-4B** at **~25–30 tokens per second (TPS)** with **8192 context length (CTX)** and all layers offloaded to GPU in **Q4_K_M quantization**.
Now, you're asking: *What kind of TPS would you expect from larger models like **Llama-3.3-70B-Instruct** or **GPT-OSS-20B** on this same hardware?*
Let’s break this down thoroughly, considering your hardware constraints, model characteristics, quantization, and inference dynamics.
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### 🔍 Key Hardware Constraints
Your **Tesla P4** is a **compute-focused GPU** from NVIDIA’s Pascal architecture (released 2016), with:
- **8GB GDDR5 VRAM**
- **1600 CUDA cores**
- **Compute Capability 6.1**
- **Max memory bandwidth: ~192 GB/s**
- **FP32 performance: ~4.5 TFLOPS**
- **FP16 performance: ~9 TFLOPS**
- **No support for Tensor Cores (no INT8/FP16 acceleration via dedicated hardware)**
- **No support for newer instruction sets like AVX-512 or modern GPU compute features**
This is critical: **The Tesla P4 lacks Tensor Cores**, which are essential for efficient inference of large LLMs on modern frameworks like **vLLM**, **llama.cpp**, or **Hugging Face Transformers**. This means even if you could load the model, it will run **very slowly** compared …
grok
Below is a complete, well-structured response to your query based on your described setup: a Lenovo P330 Tiny with an Intel i7-8700 CPU, 32GB RAM, 1TB NVMe storage, an Nvidia Tesla P4 GPU with 8GB VRAM, and Windows 10 LTSC. You're currently running the Qwen3-4B model with a context length of 8192 tokens at approximately 25-30 tokens per second (TPS), using all layers on the GPU with Q4_k_m quantization.
I'll break this down step by step, starting with a recap of your current performance, then estimating TPS for the models you mentioned (Llama-3.3-70B-Instruct and GPT-OSS 20B), and finally discussing key factors, limitations, and recommendations. My estimates are based on general benchmarks, hardware constraints, and scaling principles from models like yours, but keep in mind that actual performance can vary based on software optimizations, batch sizes, and specific implementations (e.g., via libraries like GGUF or llama.cpp).
### 1. Recap of Your Current Setup and Performance
Your rig is a solid entry-level setup for running smaller AI models, especially with the Nvidia Tesla P4 GPU, which is designed for inference tasks. Here's a quick summary of what you're achieving:
- **Model:** Qwen3-4B (4 billion parameters).
- **Configuration:** Running with a context length of 8192 tokens, all layers on the GPU, and quantized to Q4_k_m (4-bit quantization). This keeps memory usage low and speeds up inference.
- **Performance:** 25-30 TPS. This is reasonable for a 4B-parameter model…
perplexity
Based on your hardware configuration, running either **Llama 3.3 70B Instruct** or a 20B model would result in significantly lower tokens per second than your current Qwen3-4B setup.
## Hardware Constraints
Your Nvidia Tesla P4 has 8GB of VRAM, which is the primary limiting factor. The Qwen3-4B model fits comfortably in this memory footprint, allowing you to load all layers in GPU and achieve 25-30 tps. However, larger models present a fundamental problem: **Llama 3.3 70B Instruct has 70 billion parameters**[1], which would require substantially more VRAM than your P4 provides, even with aggressive quantization like Q4_K_M.
## Expected Performance
For **Llama 3.3 70B**, you would likely need to offload significant portions to system RAM, which would dramatically reduce inference speed. Based on the performance characteristics described in optimization discussions[2], models of this size on constrained hardware typically see throughput drop to **2-5 tps or lower**, depending on how many layers can remain on GPU versus being swapped to system memory. The decode phase would be particularly slow since your system would need to constantly shuffle data between the 8GB VRAM and your 32GB system RAM.
A **20B model** would be more feasible but still challenging. You might achieve **8-15 tps** if you can fit most layers on the P4, but this would still represent a substantial decrease from your current 25-30 tps with Qwen3-4B. The exact speed would depend on the specific model arch…
deepseek
Excellent question. Let's break this down systematically. Your current setup is a well-optimized, specialized inference rig, and predicting performance for much larger models requires understanding the bottlenecks that will shift.
### **1. Analysis of Your Current Setup & Performance**
Your reported **25-30 tokens/second (tps)** for **Qwen2.5 4B** (I assume you mean Qwen2.5, as Qwen3 is not standard) at **Q4_K_M** is an excellent and believable result. Here's why:
* **Model Size:** 4B parameters quantized to Q4_K_M ≈ **~2.5 GB**.
* **VRAM:** Your Tesla P4 has 8GB. The entire model fits comfortably in VRAM.
* **Bottleneck:** With all layers offloaded to GPU (`-ngl 999`), the inference is **almost entirely GPU-bound**. The CPU (i7-8700) is primarily handling the prompt processing and system tasks. The Tesla P4, while efficient for its power and form factor, is the limiting factor here.
* **Context (CTX 8192):** This is a moderate context window. At 4B, the KV cache memory overhead is manageable within your 8GB VRAM.
**Key Takeaway:** Your current setup is in the **"sweet spot"** for the 4B model class: the model fits entirely in VRAM, leading to high, consistent token generation speed.
---
### **2. The Bottleneck Shift: VRAM Capacity vs. Speed**
When you jump to models like **Llama 3.3 70B** or **GPT-OSS 20B**, the primary constraint immediately becomes **VRAM capacity**, not just GPU compute speed.
* **Llama 3.3 70B at Q4_K_M:** The model weights alone will b…
kimi
Short answer
- Llama-3.3-70B-instruct: 1.5 – 2.5 token/s (≈ 1/12 the speed you see with Qwen3-4B).
- GPT-OSS-20B: 4 – 6 token/s (≈ 1/5 the speed).
Those numbers assume you keep the same 8 k context window, Q4_K_M quantization, and let llama.cpp offload as many layers as will fit into the 8 GB Tesla P4.
Below I show the arithmetic that leads to those figures, what you can do to claw back a few more tokens per second, and where the wall really is.
--------------------------------------------------------
1. Why the big drop? (parameter count vs VRAM)
--------------------------------------------------------
Qwen3-4B Q4_K_M ≈ 2.3 GB
Llama-3.3-70B Q4_K_M ≈ 38 GB
GPT-OSS-20B Q4_K_M ≈ 11 GB
The Tesla P4 has only 8 GB, so for the two larger models the GPU will hold **only a fraction of the weights**.
Everything that does not fit is streamed from system RAM into the GPU every single forward pass – that is the killer.
--------------------------------------------------------
2. How many layers will actually fit?
--------------------------------------------------------
llama.cpp reports the size of each layer for Q4_K_M.
Typical numbers (rounded):
Model | Per-layer size | Layers | Total wt | Fits in 8 GB
-----------------|----------------|--------|----------|--------------
Qwen3-4B | 55 MB | 40 | 2.3 GB | 100 %
GPT-OSS-20B | 160 MB | 44 | 11.0 GB | 50 layers → 7.2 GB (≈ 45 layers)
Llama-3.3-70B | 270 MB …
gemini
Here is a detailed analysis of your expected performance with Llama-3.3-70B and a 20B parameter model on your specific hardware configuration.
### Executive Summary
You are about to hit the **"Memory Bandwidth Wall."**
While your current Qwen setup runs entirely inside the high-speed video memory (VRAM) of your Tesla P4, the larger models you are asking about will spill over into your system RAM (DDR4). System RAM is significantly slower than GPU VRAM.
Here are the estimated speeds you can expect:
* **Llama-3.3-70B-Instruct:** **0.5 to 1.5 tokens per second (t/s)** (Requires heavy quantization to Q2/IQ2 to even run).
* **20B Model (e.g., Command R, Granite, older NeoX):** **3 to 6 tokens per second (t/s)** (Running at Q4_K_M).
***
### The Hardware Reality Check
To understand these numbers, we have to look at where the bottleneck lies in your Lenovo P330 Tiny.
**1. The Tesla P4 (8GB VRAM)**
* **Bandwidth:** ~192 GB/s.
* **Role:** This is your "fast lane." Anything that fits here runs at the 25-30 t/s you are currently enjoying with Qwen.
* **Limitation:** 8GB is small by modern LLM standards. It can hold a 7B model at Q6, or an 8B model at Q5/Q6. It cannot hold a 20B or 70B model.
**2. The System RAM (32GB DDR4)**
* **Bandwidth:** Assuming you are running dual-channel 2666MHz DDR4 (standard for the i7-8700), your theoretical max bandwidth is ~42 GB/s. In practice, for inference, you will likely get **30–35 GB/s**.
* **Role:** This is the "slow lane." Wh…
claude
Looking at your setup and current performance with Qwen2.5-4B, I can provide some realistic estimates for the larger models you're considering.
## Current Performance Analysis
Your Lenovo P330 Tiny with the Tesla P4 is actually performing quite well for its class. Getting 25-30 tokens/second with Qwen2.5-4B at Q4_K_M with all layers on GPU is solid performance for an 8GB VRAM card.
## Llama 3.3 70B Instruct Performance Estimate
**Reality Check: This model won't run effectively on your hardware.**
Here's why:
- **VRAM Requirements**: Even at Q4_K_M quantization, Llama 3.3 70B requires approximately 35-40GB of VRAM to load all layers
- **Your Available VRAM**: 8GB Tesla P4
- **Fallback Scenario**: The model would need to run primarily in system RAM with CPU inference
**Estimated Performance if Attempted**:
- **Tokens per second**: 0.5-2 TPS maximum
- **Loading time**: 5-15 minutes initially
- **Memory usage**: Would consume most of your 32GB system RAM
- **Practical usability**: Essentially unusable for interactive purposes
The Tesla P4, while capable, is designed more for inference workloads of smaller models. The memory bandwidth and compute units simply aren't sufficient for a 70B parameter model.
## Alternative Approach for 70B Models
If you're determined to experiment with Llama 3.3 70B:
1. **Extreme quantization**: Q2_K or even lower, but quality degrades significantly
2. **Offloading strategy**: Load only 10-15 layers on GPU, rest on CPU
3. **Expected result**: …